{
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  "metadata": {
    "colab": {
      "name": "hw9_unsupervised.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Iallen520/lhy_DL_Hw/blob/master/hw9_unsupervised.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r1_Q26m0LN2r",
        "colab_type": "text"
      },
      "source": [
        "This is the tutorial of **Image Clustering**\n",
        "<br>\n",
        "If you want to skip the **training** phase, please refer to the **clustering** section directly.\n",
        "<br>\n",
        "**Training** required sections:  Prepare Training Data, Model, Training\n",
        "<br>\n",
        "**Clustering** required sections: Prepare Training Data, Model, Dimension Reduction & Clustering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GUyppmxhsgJg",
        "colab_type": "text"
      },
      "source": [
        "同學們也可以利用提供的wget指令下載訓練資料，並自行mount到雲端資料夾上，如作業一所示。這邊就不再贅述<br>\n",
        "作業的第一部分是要訓練一個autoencoder以抽取好的圖片表徵，第二部分則是將抽出來的表徵降維到二維，以便我們利用分群的方法獲得我們的答案<br>\n",
        "\n",
        "若有任何問題，歡迎來信至助教信箱 ntu-ml-2020spring-ta@googlegroups.com"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w8WjVvaONQ-m",
        "colab_type": "text"
      },
      "source": [
        "# Prepare Training Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NrIsy5olK1sH",
        "colab_type": "text"
      },
      "source": [
        "定義我們的 preprocess：將圖片的數值介於 0~255 的 int 線性轉為 -1～1 的 float。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xXTyAnhzHzHP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "def preprocess(image_list):\n",
        "    \"\"\" Normalize Image and Permute (N,H,W,C) to (N,C,H,W)\n",
        "    Args:\n",
        "      image_list: List of images (9000, 32, 32, 3)\n",
        "    Returns:\n",
        "      image_list: List of images (9000, 3, 32, 32)\n",
        "    \"\"\"\n",
        "    image_list = np.array(image_list)\n",
        "    image_list = np.transpose(image_list, (0, 3, 1, 2))\n",
        "    image_list = (image_list / 255.0) * 2 - 1\n",
        "    image_list = image_list.astype(np.float32)\n",
        "    return image_list"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qj_hairpGhLj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torch.utils.data import Dataset\n",
        "\n",
        "class Image_Dataset(Dataset):\n",
        "    def __init__(self, image_list):\n",
        "        self.image_list = image_list\n",
        "    def __len__(self):\n",
        "        return len(self.image_list)\n",
        "    def __getitem__(self, idx):\n",
        "        images = self.image_list[idx]\n",
        "        return images"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8EvJPEeGLgt7",
        "colab_type": "text"
      },
      "source": [
        "將訓練資料讀入，並且 preprocess。\n",
        "之後我們將 preprocess 完的訓練資料變成我們需要的 dataset。請同學不要使用 valX 和 valY 來訓練。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G_tMv9S5oqn9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "trainX = np.load('trainX_new.npy')\n",
        "trainX_preprocessed = preprocess(trainX)\n",
        "img_dataset = Image_Dataset(trainX_preprocessed)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z64cTA5jaNtg",
        "colab_type": "text"
      },
      "source": [
        "# Some useful functions\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wCtxq6GSL4tq",
        "colab_type": "text"
      },
      "source": [
        "這邊提供一些有用的 functions。\n",
        "一個是計算 model 參數量的（report 會用到），另一個是固定訓練的隨機種子（以便 reproduce）。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KWJNJs-UaUFb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import random\n",
        "import torch\n",
        "\n",
        "def count_parameters(model, only_trainable=False):\n",
        "    if only_trainable:\n",
        "        return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "    else:\n",
        "        return sum(p.numel() for p in model.parameters())\n",
        "\n",
        "def same_seeds(seed):\n",
        "    torch.manual_seed(seed)\n",
        "    if torch.cuda.is_available():\n",
        "        torch.cuda.manual_seed(seed)\n",
        "        torch.cuda.manual_seed_all(seed)  # if you are using multi-GPU.\n",
        "    np.random.seed(seed)  # Numpy module.\n",
        "    random.seed(seed)  # Python random module.\n",
        "    torch.backends.cudnn.benchmark = False\n",
        "    torch.backends.cudnn.deterministic = True"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_ZuRV_dNjhD",
        "colab_type": "text"
      },
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0mxBEwGYMSjm",
        "colab_type": "text"
      },
      "source": [
        "定義我們的 baseline autoeocoder。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dci5VCIuQwvI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.nn as nn\n",
        "\n",
        "class AE(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(AE, self).__init__()\n",
        "        \n",
        "        self.encoder = nn.Sequential(\n",
        "            nn.Conv2d(3, 64, 3, stride=1, padding=1),\n",
        "            nn.ReLU(True),\n",
        "            nn.MaxPool2d(2),\n",
        "            nn.Conv2d(64, 128, 3, stride=1, padding=1),\n",
        "            nn.ReLU(True),\n",
        "            nn.MaxPool2d(2),\n",
        "            nn.Conv2d(128, 256, 3, stride=1, padding=1),\n",
        "            nn.ReLU(True),\n",
        "            nn.MaxPool2d(2)\n",
        "        )\n",
        " \n",
        "        self.decoder = nn.Sequential(\n",
        "            nn.ConvTranspose2d(256, 128, 5, stride=1),\n",
        "            nn.ReLU(True),\n",
        "            nn.ConvTranspose2d(128, 64, 9, stride=1),\n",
        "            nn.ReLU(True),\n",
        "            nn.ConvTranspose2d(64, 3, 17, stride=1),\n",
        "            nn.Tanh()\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x1 = self.encoder(x)\n",
        "        x  = self.decoder(x1)\n",
        "        return x1, x"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mF_7fi7xM5Er",
        "colab_type": "text"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p8Wr2tNYNgcP",
        "colab_type": "text"
      },
      "source": [
        "這個部分就是主要的訓練階段。\n",
        "我們先將準備好的 dataset 當作參數餵給 dataloader。\n",
        "將 dataloader、model、loss criterion、optimizer 都準備好之後，就可以開始訓練。\n",
        "訓練完成後，我們會將 model 存下來。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uKZ9rhK-2b76",
        "colab_type": "code",
        "outputId": "ea7e3428-a8f2-4d24-eb42-c55639591c1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "tags": []
      },
      "source": [
        "import torch\n",
        "from torch import optim\n",
        "\n",
        "model = AE().cuda()\n",
        "criterion = nn.MSELoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=1e-5, weight_decay=1e-5)\n",
        "\n",
        "model.train()\n",
        "n_epoch = 100\n",
        "\n",
        "same_seeds(0)\n",
        "# 準備 dataloader, model, loss criterion 和 optimizer\n",
        "img_dataloader = DataLoader(img_dataset, batch_size=64, shuffle=True)\n",
        "\n",
        "\n",
        "# 主要的訓練過程\n",
        "for epoch in range(n_epoch):\n",
        "    for data in img_dataloader:\n",
        "        img = data\n",
        "        img = img.cuda()\n",
        "        output1, output = model(img)\n",
        "        loss = criterion(output, img)\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        if (epoch+1) % 10 == 0:\n",
        "            torch.save(model.state_dict(), './checkpoints/checkpoint_{}.pth'.format(epoch+1))\n",
        "    print('\\r epoch [{}/{}], loss:{:.5f}'.format(epoch+1, n_epoch, loss.data), end=' ')\n",
        "\n",
        "# 訓練完成後儲存 model\n",
        "torch.save(model.state_dict(), './checkpoints/last_checkpoint.pth')"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": " epoch [100/100], loss:0.04742"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "HhU5gcRhlTE1"
      },
      "source": [
        "# Dimension Reduction & Clustering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jrn7UhtLyB4n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "def cal_acc(gt, pred):\n",
        "    \"\"\" Computes categorization accuracy of our task.\n",
        "    Args:\n",
        "      gt: Ground truth labels (9000, )\n",
        "      pred: Predicted labels (9000, )\n",
        "    Returns:\n",
        "      acc: Accuracy (0~1 scalar)\n",
        "    \"\"\"\n",
        "    # Calculate Correct predictions\n",
        "    correct = np.sum(gt == pred)\n",
        "    acc = correct / gt.shape[0]\n",
        "    # 因為是 binary unsupervised clustering，因此取 max(acc, 1-acc)\n",
        "    return max(acc, 1-acc)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hl9skAvMOvSV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_scatter(feat, label, savefig=None):\n",
        "    \"\"\" Plot Scatter Image.\n",
        "    Args:\n",
        "      feat: the (x, y) coordinate of clustering result, shape: (9000, 2)\n",
        "      label: ground truth label of image (0/1), shape: (9000,)\n",
        "    Returns:\n",
        "      None\n",
        "    \"\"\"\n",
        "    X = feat[:, 0]\n",
        "    Y = feat[:, 1]\n",
        "    plt.scatter(X, Y, c = label)\n",
        "    plt.legend(loc='best')\n",
        "    if savefig is not None:\n",
        "        plt.savefig(savefig)\n",
        "    plt.show()"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c0QvyGtKOp_p",
        "colab_type": "text"
      },
      "source": [
        "接著我們使用訓練好的 model，來預測 testing data 的類別。\n",
        "\n",
        "由於 testing data 與 training data 一樣，因此我們使用同樣的 dataset 來實作 dataloader。與 training 不同的地方在於 shuffle 這個參數值在這邊是 False。\n",
        "\n",
        "準備好 model 與 dataloader，我們就可以進行預測了。\n",
        "\n",
        "我們只需要 encoder 的結果（latents），利用 latents 進行 clustering 之後，就可以分類了。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DBnn6RlncV-j",
        "colab_type": "code",
        "outputId": "4f3da19c-9f55-4204-92e8-3ee2a5c24698",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "tags": []
      },
      "source": [
        "import torch\n",
        "from sklearn.decomposition import KernelPCA\n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn.cluster import MiniBatchKMeans\n",
        "\n",
        "def inference(X, model, batch_size=256):\n",
        "    X = preprocess(X)\n",
        "    dataset = Image_Dataset(X)\n",
        "    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)\n",
        "    latents = []\n",
        "    for i, x in enumerate(dataloader):\n",
        "        x = torch.FloatTensor(x)\n",
        "        vec, img = model(x.cuda())\n",
        "        if i == 0:\n",
        "            latents = vec.view(img.size()[0], -1).cpu().detach().numpy()\n",
        "        else:\n",
        "            latents = np.concatenate((latents, vec.view(img.size()[0], -1).cpu().detach().numpy()), axis = 0)\n",
        "    print('Latents Shape:', latents.shape)\n",
        "    return latents\n",
        "\n",
        "def predict(latents):\n",
        "    # First Dimension Reduction\n",
        "    transformer = KernelPCA(n_components=200, kernel='rbf', n_jobs=-1)\n",
        "    kpca = transformer.fit_transform(latents)\n",
        "    print('First Reduction Shape:', kpca.shape)\n",
        "\n",
        "    # # Second Dimesnion Reduction\n",
        "    X_embedded = TSNE(n_components=2).fit_transform(kpca)\n",
        "    print('Second Reduction Shape:', X_embedded.shape)\n",
        "\n",
        "    # Clustering\n",
        "    pred = MiniBatchKMeans(n_clusters=2, random_state=0).fit(X_embedded)\n",
        "    pred = [int(i) for i in pred.labels_]\n",
        "    pred = np.array(pred)\n",
        "    return pred, X_embedded\n",
        "\n",
        "def invert(pred):\n",
        "    return np.abs(1-pred)\n",
        "\n",
        "def save_prediction(pred, out_csv='prediction.csv'):\n",
        "    with open(out_csv, 'w') as f:\n",
        "        f.write('id, label\\n')\n",
        "        for i, p in enumerate(pred):\n",
        "            f.write(f'{i},{p}\\n')\n",
        "    print(f'Save prediction to {out_csv}.')\n",
        "\n",
        "# load model\n",
        "model = AE().cuda()\n",
        "model.load_state_dict(torch.load('./checkpoints/last_checkpoint.pth'))\n",
        "model.eval()\n",
        "\n",
        "# 準備 data\n",
        "trainX = np.load('trainX_new.npy')\n",
        "\n",
        "# 預測答案\n",
        "latents = inference(X=trainX, model=model)\n",
        "pred, X_embedded = predict(latents)\n",
        "\n",
        "# 將預測結果存檔，上傳 kaggle\n",
        "save_prediction(pred, 'prediction.csv')\n",
        "\n",
        "# 由於是 unsupervised 的二分類問題，我們只在乎有沒有成功將圖片分成兩群\n",
        "# 如果上面的檔案上傳 kaggle 後正確率不足 0.5，只要將 label 反過來就行了\n",
        "save_prediction(invert(pred), 'prediction_invert.csv')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Latents Shape: (8500, 4096)\nFirst Reduction Shape: (8500, 200)\nSecond Reduction Shape: (8500, 2)\nSave prediction to prediction.csv.\nSave prediction to prediction_invert.csv.\n"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "snHP0s9kciEn",
        "colab_type": "text"
      },
      "source": [
        "Problem 1.b (作圖)\n",
        "===\n",
        "將 val data 的降維結果 (embedding) 與他們對應的 label 畫出來。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iyNe4gUEcAhZ",
        "colab_type": "code",
        "outputId": "84031586-97c8-4164-fe64-858b0bb52fd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "tags": []
      },
      "source": [
        "valX = np.load('valX.npy')\n",
        "valY = np.load('valY.npy')\n",
        "\n",
        "# ==============================================\n",
        "#  我們示範 basline model 的作圖，\n",
        "#  report 請同學另外還要再畫一張 improved model 的圖。\n",
        "# ==============================================\n",
        "model.load_state_dict(torch.load('./checkpoints/last_checkpoint.pth'))\n",
        "model.eval()\n",
        "latents = inference(valX, model)\n",
        "pred_from_latent, emb_from_latent = predict(latents)\n",
        "acc_latent = cal_acc(valY, pred_from_latent)\n",
        "print('The clustering accuracy is:', acc_latent)\n",
        "print('The clustering result:')\n",
        "plot_scatter(emb_from_latent, valY, savefig='p1_baseline.png')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Latents Shape: (500, 4096)\nFirst Reduction Shape: (500, 200)\nNo handles with labels found to put in legend.\nSecond Reduction Shape: (500, 2)\nThe clustering accuracy is: 0.73\nThe clustering result:\n"
        },
        {
          "output_type": "display_data",
          "data": {
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Xe62bgW8++J7vJyzLSPdzOz3MHPINEYXCuffpu/nmxBTWLd5MzMlYqt1ehdvvvcXvCyelB5nQH9ybAakpQYpIKPItwlhO/6Km28D2ADh/BhlMHVKA9YGCvdkg1G9ZB9UbxMhLmZEvb4uw8erMfrw8vQ+qT2XV12v4fMD/MlwvQhFYwyw8O/JxylQrxYHNR9jxx56Mh0HJSsW5p1sLvh31Q0B6pc/jo3rDKmTH0Z0nSLiQSFaPpjPNxY+TV+hW52aH2+UN1sNcP/0zG/5eth29SLPP42PlnL/oNepJv+3adXWMvBA89FJ72j93D5YwC8XKFMnTPPRIS9YePEXLRBfow0LYuiCT3wcCOyYRNazArnOzURDumjuB7sBuIcSO9G3D0Iz7AiFEL+Ak0LUArnXdkN4zyLRpmjE1lEGE90FY/CsnpXSDcyXSvRkMpRG2RxCGkkHHVFWVReN/CvhCu+wuvh65kHufvhuz1Uzrbi2Czyvtq/TX1vRfdAnINGTiQESxH3TPEUJA1CiwPoR0/gxqDLg2pMvqCpBeKPQRwlA6Nx/NVWMNs9D/s55M7BsYZ7CGW7j93vp+2xRFQVEU2j/XmqKlo/lm1PfEnLpE7WY16DHicSrU0gKXH/z0Jsf3nOLojhOUqlScunfWwpHqZNkXK0nweDPiHZYwM807NaFstezv15Hi1H17AEhNsvv9X0rJkX+Ok3QphVpNqhFROPCtqHDxKEpWLBGQuWIwGWjeqUm2cwmYW6pT923I5/VhzzI3gOadGjNl8OyA7QajwtlD55k/Zimtu7XQZCdyEafQIy3Zzvjnp/J3+ltDRHQ4g7/owx0P6rqK84wQAllsOcQ/DWr6ZyjCoNBYFFON7E/+DxNSodRBek8h4zqDtHPFl22DqPdQwjprx6hpyPgnwHsasANmEAZE9EyEubHuuI5UB52LPItPZxVrCbOwLPXrHOemxrYFX5DgdPQ8FEvuVpdSOsC1HlDB3ByhRHB8zymWTPqFmFOxNGzbgA7PtymQ/PxgjOr2CesWb8Lr1j5js9VEqcolmLptDGZr/hp/6JEQk8TXIxeyfukWbOEWHuzXjk4vtc+x4MftdNOlRK+MatvLWMLM9Br9FJ0H3A9olbpD248i9vQlFIOC1+3lmfe68vgbDweMuW/jQYa0+wCf24vH7cUSZiGySDhTtnzsl+1z6Vw8s9/+jk0/b8cWbuWhF9vxyOAHMuZ88WQsPWsPCvCxW8MtvPfD6zRsGxhbWTH7Dya9qMkla8FXFRCoPh9Saue2faYlAyf3zvlDBY7tOsnP038j4WISdzzYiJVzV7N33QG/lExLmIUJf42gRsOquRozt6iqC/CgpNd6/NcJ9XjNI2riG+D8kQBlRlEIUWIjQhhRUz+H1GkEtKFTSiKKr8lYDaneWEh+M13JUWH9ikJMfK04SfH+L1G1mlZn0sYPc55bzD2gBmmvZ2yAUix/OfUblm7hw6c+xePyovpULGFmChWPZOrWj4kqqi8VfLWoqspvc/7ixykrcKa5aNm1OY++3DHHB8vpg2dZPHE5Zw6do37LOjzYrx2Fil2brkp/fLeWCc9/gcetfS7WcAtlq5dm4voPsNi0/P/e9V/h1P6zfitra7iF4d+/TqP7NGO7fdUuVsz6A7fLQ8O29Yk5Fce5oxe45a7a3NejFWGRV5QvUxJS6VVnMMlxKRkLgstvH8O+uSKS9tV781k47ifcDpdmpCOsNLy3PsO/fy3oajzm9CXW/bCJS2fjWfzZcrxZ5CZya5RXff0Xn/ad7vf74nK4A+LpQhG07Nqct74NkhUTokAIGfk8osa0BPW8zh4bothPCGMF1Nj24DsW5JjFCGMVVPc/EP8kmTNbVFXhwikTve6qierTvogWm5kPf3mL+nfXyXluKWMhbUaQvQqi5G6Ers5McHxeH11L985IQTQYJT3eOM+Dz8ZhC1cRplqIqOEF1sz6atj++26Gd/oYt8uD6lMxW02ERdqYsm0MxcsVvSbXPL7nFMu+WEn8hUSadWzIPd1aYLZon/HJfad5scmbuj71JvffzqhlbzJjyFx+nPJrRk66NdzCbW1uYcTiN3SN8fwxS5gzYiHuLM2+zVYTM/d8QukqV1yCe9btZ8XsP3HaXbR+ogXNHmyYKz/4t6N/YPZb3+nu6z78MZ4ZHty76rS7eKxkL90cez2qN6zClC0f5+rYEPnjuqRQ3lQYigUx8j64XAAUtI+oCpi1fqHxPciauqgoKiXLCx7pa2PFtwaq3FKR50Z1o96dtXI1NRHeF5n2P/T13xXykzB1+uA5vzTFwWNPc/eDiVjD0hcA3gPI+J5QdBHCVD3P4xcUUkrGPz8FZyaD6nZ68Hl9fDV8Pq/9r/9Vj69ndCvXq8CAz5/XPSclIQ2DUf8zT4pN5tzRCyyZ9IufW8WZ5uKfP/awfdUuXbfK7nUHAgw8gNFs5MiOE35Gvl6L2nnS5blciLUxm65dl6UignFg0+GgsYqsGM1G6rfMefES4tpxA+ZJ/fuI8BeArI0jzGBpjVDS3QK2bjrHCDBW1LJcnCsIlptuMLh4YXRzFsd9yfjVI3Jt4AGEEpl+7azPZyNY2mj673kkLMqGz6vNtVBRD606ZTLwGbhIODGW+WOW8tucv3CkXtsernrEX0gk4WJSwHafV2VzPlIaL/P7t2t5qlI/7jN0pVuFPrluYHJ890k2Ld+uuSmyYLaaaNG5CdtX7dZNXXSmOtn4k345fvmaZXT18lWfSqlKxXM1Nz0unY2jZ53BDOswioNbjwY9rlzN7CtdbRFWv1qOzGS+V0URWMMtdHnlwfxNOESBEDLyOghrO4gcqIlyiXDAApaWiEIfXTkmrCtYWqHln1u145RiiMKfaweosQSvMDWCMfv0vWznF/maljMswtKvHQaGSohCI/I1Xonyxah6W2UUg0KpCm48bj1/rkrC2U3Mfvs7PntpJt3K9+XIP8fzfQ/5wRpuCWpcwgvlL0D857z1fPLCF8Sc0nLmL52J5/MBs3I09F+9t4ABzYaxYMzSgCwXi81MsXJFebB/O8KibLqrXqPJQGQR/bqEh/q3w2g2BBxfvmZZqt2W/3zwkV0ncO7IBRypzqCfo8lipFz17GWVazSqSlTRSN10UINB0dY6JgN3dm7K1G1jCiQtM0T+CRn5ICjhvRAlNiGKfIcovholejJCuWJIhDCgRE9EFF2IiBqGKDQeUXw1wlhJO8DciKAFSEohsLTO9vpSurVCJ8dSVM8+pHunpiIJCCUcUWQ+InqWdu3CUxHFliGU/H+Z3l34KuVrliE+NhKTOdAAeL1weKcVn9eHM9VJWpKdEY+OC9DauZaER4XRqN2tGM3+q1xLmIWHB96fqzGO7z7J3PcX8u2H33Pm8Hlmv/1dgHSCy+7iy3fmBR3jxN7TLBy7FJfDjepTMwymYlCo1bQ6PUY+ztRtYwiPCuOOB/XjGD6fyu41+9ny646AfaUrl2T0L29RtnppjGYjRrORRu1u5aNf3853euOlc/Ec2X48aBEaaCmKJSuWoFK9CtmOJYRg1PJhRJeKTi+kuiJA5/X4QGrx10tn4ylVqUS+5hui4AgFXq8SqdqRqZ+AYwngAXMrRNRQUEoiE54D9zb8MnBEESj6E4ox+Gu39OxDxj8L0oOWD+8D0mMAti6IqHfy5ZbJ8V6k5NC2Y0SZx1Ki+HqEuDJvR5rCS+2rc+bolQeX0WSg6xsP88ig+69ZdktWUhJSeeuB0RzbdVITMHN5aNu9JYOm9s4x4Dj7ne/4fsIyPG4vQhEYDAY8LndAsdNlfvXO1x3zm1HfM/e9BfiyGEzFqPDc+0/wxJDOftv3rNvPOw99jM+n4kzzX0Vbwy08MbQzT731aMB1pJQkXUrGYjNji9DrOxscVVXZ9PN21i/ZTFiklVtb38LopybqBkuFEFgjLERGRzBm1bs51g9cxufzsXP1Pr585zv2bzockFljtpnpNvRh0pId1GhYlRaPNMnR3x8if4Sya64RUkpk/JPg2Q1cXg0aQCnCiQuzOLnvIuUr76JK1d+QPhVsj6BEPoXQCdqq9sWQ9jn4Lrt5grl6bBDRHyWiz7W5KUBKn1YIlvYVyBSO7o1iwitFObI70CVispgwGBVGLBnC7W3yLmgVcyqWKYO/ZP+mQxQvX4w+Y5/xU78MxvE9p4g5GUvVWytRrGzOWTVHd55gUPO3Av3nAl0ZnWLlivDdKX3Bq3kfLear4fN1xeQKl4hiztHJ2ML93+I8bg9fvPIVy//3O94sTVTMVhPfnZ5GVNHIHO8jN/h8Pt7t9DG71uzHmaoVdBlNBowmI/YsonBGs5Em99/Go4M7Uq9FrXxVqA64YxgHNh3W3We2mnA7PdgirESXLMSkv0cX2H2GuEJ2Rj7krrkaPLvAs48rBh6cdpWhjxVi4B3D+aTPDAbes4GHKkXSoYyVh0r9xsT+X+Jy+K+m1LQ5kPye1k8UJ9mrRTrA/lU2+68eIQwoEf1RSm5CKbWPPXvf4fRRfVlej8uDM83F+13H4/Xk3AEqM8f3nKJ71ZdYv2Qz8ecTObj5CK+0fJfvJy7L8dzK9SrQ9IGGORr4tKQ0Th88y+oF63VbEBpNxoDWhZYwMz2zyAJk5q4uzYJml9iTHayYFejPN5lNnD1yIcDAg2Zo9wcxkno40pwc3n6MuPP6PXg2LN3Krr/2ZfQlUH0qbqcHt9ONxWbGkH6/ljALxcoW4dWZ/ah/d518SxA0vK8BJov+Cv1yVpEj1UnMqUvMCpK2GeLaETLy+SA5PoV1izdxctfvAYvAWR+WZs9mGy6HD0eKE4/Tg8uuFau4nR5++2o1o7p9mnG8lF5InYimFJlL1OSrvoe/l23lsxdnsmTSclITs3Z88qdj3/uo2bga1iDdl0AzJHrdpbLjwyc/1fURT39tLqoa3HecGzxuD+N6TeGxUr15sfFQFo1fll7h6Y/BqNC2RytKVy2JYlAoWak4g6f1oW33lkHHLlutNPf1aKW7z+308PdP+m+jRUoX1m1sIlWZ69Xt/DFLeKzk87zW+j2eqfoibz84OmB1vmbhRl23jNlqpvfHT/PwS+1p/nBjeo95muk7xxFV5OpW1p0HdCAiOjxon9/LeD0+1izaeFXXCpF3QnnyeWTp5F+Y/vpcjGYjdRsn8dYXLmyZkiR+W1AEjyv4s9Pt9LBt5U5iTsVSokJxUBNA5qyZ7ocp/3LAqUlp9Kw9iIQLV1IRp702lzGr3g3qJjGZTYz9fTj//L6bif1ncP7oRd3j8hoUPLnvjO72yw+MvKSWZmXK4C9ZPW89HpcnW6liqUqeHfk4RUr1zdP47Xu25re5awKKoIQQFNHRm0+MTSL21CXditDoUoWp1aRajtdc+/3ffD1ykd81t6/axZhnP+e971/P2GaLtGo6L1ldsQJKVSlJp5c65OIOc0+hYlFM+2cs8z5ewubl24kqGsmBTUd0H9SGLG9Ah7YdZcYbczm49RiFi0fR7c3OtO/ZOt8B5uxQVR+kjgFHulai7WGIeA1FubnNYGglnwcObTvKjCFf43Z6sCc72PK7iQunzH4phy5nzr+cJouJc5cNpVJIU5LMFQqIMETUW/mYvcbQ+973M/Cgadu/1fFDfL7gmvOKotCwbQN6jXoSa3hgz1SD0UDtZnkrlMquSYYtIv99Wd0uDyu//FM3fx2hff5mmxmz1cTAqb0zOljlhRqNqlKsbDRKlhx4s81Epxfb+23z+XwMbvEOu9cGap6XqVYq11kz88cs8SsEA/C4vGxe/g8pCakZ29r3bI3ZFug+EYrgtnzETXJDdMnC9JvwLLMPfMbE9aO4tXW9AJeWyWKiTabeCMd3n+TVlsPZ8edeHCkOzh+7yORBs/n2Q32hvatBSgmX2oB9Nsh47Y99Fly697pmiP0bhIx8Hlg+43c8foJQgte7VGXzH9GoqgFQuPUuJccvrNvpoUJtTTlRCDOEPUtyQjinj1hwuy6fawHLvWC+B0wNwVgDbJ0RRRcjTPmrIExLSuNQkCIYZ5qLg5uP5DjGXV2a0ezBRljDLRiMCpYwi6bTsug13QKe7LjzYX0hN1uElSr1K+VprMzYk+1Bv7jhUWH0/vhp+ox9hq+OfNXZINsAACAASURBVE67HoENNnKDEIKPfn2HcjXLYA23EBZlwxpu4cXPelKrif/DbuuvO4m/kJBRcHYZk9VEl5c7UrpycOXSzOgVgoHmcsrcFatOsxo8/U4XTFYTtggrYZE2wguFMWrZsAw5hmvN67P7U6xsEWyRNkxmY/rPtAI9Rlxp1j5nxMKAB7HL7mLeR4sD4lZXi3QsuqJcmRn1HNJR8A+VG4mb+z2lgElNTEPNUkSSkmBk3KAavDLzBe7u0oSXpl5iwB3DcDvcul14LDYzrZ64M2P16LS7GPe8yoYfq2M0+kBIer2VxoMDB6HYcpf7nVvSkuyasLiOAZRSBk0lzIyiKLz17WAObjnC9lW7iSwSwd2PNcuXX/eNOQM4uOUoF45f6SdjMBn4eNW7V/W6HlU0kojCESRcTPTbLgTUa1GLzrnMqc+JUpVKMHPPJ5zYe5r48wnEnUvAkerk5L7TVKyTqSn5wXN+yoyX8Tg9THrpf+zdeIiBn/fKMU3y1nvqserrNQFxDKPJSIkKxfy2PTGkM/f1aMX2VbsJi7TRqF2DAlH2TE3UZBxymmuxskWZc+RzNv28nQvHY6jSoCINWtX1+7ke2nZU92EshCDm1KWC7X3ryCaY7/wJwgJTWG8WQkY+D9z1SFM2/bwdZ5q/9KzX4+XWe+ojhJlyNcow+8BEls9YxcGtRylerigndp/iwJYjhEeF0WlABx5/o1PGueOfn8rGn7bicckMX/6M94tTsm5JmnUs2PkXK1cUW4QVe3JgkNdoMubKL3yZmo2rZbTzyw1SqloDcBGekeNvNpuYe3Qy21ftYsPSLZSvWYYOz7e5amOkKAr9Pn2W8b2m+DUYsYRZss2aAZCe3ci0r0G9CJZWCNtjCCWc1MQ0/lqwgcTYZBq0rEPdO2shhEhvCejh/a4TUFVV6/okBK27teCVGX0RQlCpXnlMZmOA4iNo8Ye/5m8g4UIiH614O9u5dR/+GBt+3IIj1enXXcqR6uCJsn3o+WE3HujdNmN7kVLR3Pv01baO1Diy4zjjnpvCyX2nAWjQqi5vfPVStq4ug9FA8076b2ugBbBjTgZ25/J5fbpxjatCyaZpe3b7bgJCefJ5wOf1MeS+9zm45QjONBdCCMw2Ez1GPM5jr+atPydoq6KupXvrBgbr3FGDietHFcS0/VizaCOjnvgk4I1k6NcDafPkXQV+PQDV/gOkjtWygoQZwp7DRS9Wfb2OvRsOUqF2WTr0auOnp66H0+5i5+q9KIqgQau6OT4Mdvy5h28+WMT5YzHUbFKNZ4Y/5rfC1p1n8ntoKbEqYAVDKQ4d/Zg32k1A+iRupxuzzUyDVnU1FUlF8GT5vgHpjNZwC6/NepGWj92Bqqr0ve11bUWvY+hBKxyasWs8ZapmLykQc/oS8z9ewpqFG0mOT/Vb1VvCLLwyo2+2TWey4na6MZgM2WrrJ8Ym0aP6AL/FgcGoUKpySWbt/zTfqZe71uxjWIdRfi4bi81M22daMmjqC/kaMxiq5yjEBQk4F12BYsq/zMiNQKgYqgDxeX2sWfQ3axdtJLxQGB1630udZvnrSnP2yHn63va6brpbiQrF+ObE1Kudri4HNh9mxpCvOX3wHBVqlaX/p89elQ88O6TzN2Tiq2Ru2SaxsnhmSb78qAQuuxuz1YTRZGT86hFBtVnWL9nMR90/QygCn8eH1+OjdtPqPPX2ozRuf9vVz1M6kTHN0hvFZMbCd5PK8OVo/4pea7iF/p8+R+VbKvDGvSMDGosA3N62Ph//+g6gPdCnvT6HlbP/DHjAgqa98+7CVwO6YumhqiqPFH1Oc79loWz10nx58LMcx9i9dj8T+03n9IGzGC0m2j13D33HPaP74Jw/dglz3guUPrZFWnnvhzfyVQR3mfVLNjN50CziLyRiNBno2Kctz3/0dJ7jO7lBTZ0GqeP9N0a8gRKhrzD6/4mQ1HABYjAauOeJO7nniTuveqySFYvrZpgoBoVbcqEtn19qNanO+D/zJ2aWV2TqZ2Ttyblnk4EV31jSUwFFeqGOh7E9JzPtn3EBY1w6G8fopyYGBOn2bjjIiC7j6DzgfnqNfurqJurZj34egotGLWMCjLwzzcWKWX/Qe0x3XZVJwO8NLaJwOK/O6EdkdIRusw6Py0PFusHfMvxm5HAHuAwvE3MqliM7jlO1QaWgcY2T+07zZodRV/oMO9z8OutPEi4kMnzRawHHnzlwTlf6WPqkXzwlP9z5cBOad2qMPcWBNcySbcbV1aJE9EENewoc6Y11bF1RrlPz+n+TUHbNv4jRZOSFsd39BJ4Ug4I13EL3d7v8izMLRKoJSMcypHMFUs2+eMoPn39Gw/H9Vt56sjInD9rIWhl0ct8Z3cKsP+dt0F39Arjsbn6Y+DMxpwN9u3lCidR63eqQkqi/FpISajWppuuusIRZdAuqHhn8AJYws9+DwRJmofWTd1E0l35oa5iFQsX1tYK8Hh8v3/UOL9R/lUvn4nWPmT92aYCL0O10s3n5dmLPxAUcX6d5Td20WQlUvz3/qpiXEUIQHhV2TQ38ZRQlAiX8Oe3Pf8DAQ8jI/+vc//y9DF/0KvXuqkWpSiVo8+RdTN02JtciUXkhLdnOoW1HSYjRT8ULhmpfgIy5G5n0DjLpTWRsc6Trr9ydbPR3Zc3/vAQeV5DMGSkzSu795p2UhscdvKDJYFTY8cee3M0nCMJYDYwVCCxJtbFqUaAqoyXMQvuerTGajAz7dhCWMAsmi/YwsEZYqd20Om2fCTTyxcoUYfLmj2jeqTFhUTaKly9KjxFdGTzN3wetqiqbf/mHzwfMZM6IBZw/fqUATQhB7zHdsYQFulakKnGmuTi5/wz9G77B6KcnsurrNX6f38k9p3UrjU0Wk+7K/J5uLYgqFun3szFbTdS7sybVb///7cv+LxDyyV9jpHsn0v4tqJfAci8irDNCBJcHuCZzkJIv353HovHLMJoNeFxemj/cmDdmv5hj8FJ6jyMvPURAL1tsiBJrrzRRCXa+exsy/jkuu2z6tqnB8f366Xd1mtdg4rrAYPPeDQcZ2u79oO3mwiJtDP16IHc8qOuSzDWqYzEkvYlf162wZzmw91GGtv8AqUrcDi3wWr9lHUYuGZKx+ow9E8dvc/8iKSaJ29s2oHH7W/1W+NJ7EmQKGGvoCtRlJiEmkWEdPuTUgbO4HW6MZi0wOmTOAO56tFnGceuXbOar4fM5ue9MthLC1nAL5WqU4ZO172MNszDppZn8PH1VYN6+xcQ3J6cSXSKwp29ibBKz357H+sWbMVmMtO/Vmm5vPlJgefexZ+JY+dVq4s7F07BtA5p1bHhdVvY3C6HA63Xk8ucphEBNmwcpH6IZSAnYwFgBUXQBQuRNOvZqWD5zFVNf/tLPSJptZto82YJXZvTL9lw15TNI+4JA0bQwRKF3EbZHcry+dG9FpowF7yHGv1yRVQvNqL7Akvv/7f2UCrUCc6OllIx+eiLrl2zG7Qhc0UcVjWDe2elXJWMrvaeQlzriHz8Q6Y3Z/yQtyamlUMYk06DVlRTKHMf1nUcm9APvMRDpbp+okSg2/fzYheN/5H/DvvVLkbyMLcLKwoszMxqIX6Zn3cGc3h+kuXs6FpuZ50Z149HBHTl//CJ9bn0dRybNG0uYhTZP3cXL066dumkwtq7cyYhHxuLz+fC4vFgjrFSuV4FxfwwvkNz+/wIhFcrrQFpSGuN6TqZj+FO0Nz/BsPtHcmHvWDSjcdmgOcB7EmlfBICUDqRzpebrVvX9pwXBgrE/BqyC3Q43v3+zFrdTXzdHek8hnavAdxb9NoYqSP3gX1aEuRFK0fkoJf/hieGTMVv9jZTJYqJjn/t0DTxoD8yhcwfy1rcvU7NJVRSDVmlri7QSXaowH69896oM/PE9pzj69xhUNeuDTGqrb/ffRBQO54EX2vLU249Sr0Xt3Bl4KbXeuN6DgFOrE5CpkDQM6dkbcPzutfv5avgCXQMPWq7/3g3+InBzRizg7CG9fsT+uBxuVs9bD2hNSSau/4CGbetjCTNTpHQ0T7/ThYFTss8ykVIivWeQvquMf2TC5/VpOvd2V0bBmDPVybFdJ/h5xqoCu85/mVB2TQEgpeS11iM4ufd0Rh70tpV7GLi5IrM37CM8KvOrtBOcvyKNNZCJ6aJYEsCLjHwDJbw70ncevEfBUIGYszaO7z5F6aolqVi7XL7ml3RJX7VSVSWOVKffaklKj5by6PoThCldPE1PdF2COe+FNuVrlmXCXyOYMng2BzYfIaJwGJ0HPeBXIKaHoig079SY5p0a47S72LfhIJYwC7WaVss2xzs7fF4f7z8+ga0rdvDqJ0epUk0v8Co1V1t+8O5Jbwif1Wi7kfa5fu0kAZZOWYE7u3J+CaZMXbG2/baTheN+zNZVkxlb5JW3x8r1KvBRenpnbpDurcjE10GNI+asQsyFGlRsMppCxavmegw9ju48oVs74LK7+f3rtXQeULBV3wVJzKlYpr0+ly2//IPZaqJ9rzY8M/yxG+7tI2TkC4A96w5w9vB5v19WVZU4HYJVi6Lp1DNLxoKI1Ay8zJJJkjIW1bUa3JuRwoTX5eDY2gg+ea0m9hRJzSbVeP/HoYRF5s3VU69FbTYt2xogW1C4RKEAiVtX3FS2/LSV1MRwGtyZSqkKbrQXPgOasVIAM0S8oDUsD4JU7UjHAnD+DoaiiLDuCLPWCq/67VX4ZM37ebqHzFjDLLnKJ8+JJZOWs/XXHbgcbrb8EU6TNonYwrMYTOkDUz7z8NU49F+WVfBdCNiaEpeSrbSEyWKizh1XAtk/Tvk1aJwiK9ZwCw/2vS9Xx2ZFczn1wpnmYlTfCuxYG4nJ4sXjGkLHfg/Qd/yz+ZahMJmNQfvNmq03bhep1MQ0Xmw8lOS4lIzF0uKJP3N0x3FG/5J95fL1JuSuKQBOHzirK6vqcigc3ZvVINvAFKzzkQvcGwAXQqZiMvu49c5knhx8FJfDzf6/DzGx3/Q8z+/5j57CGmHNUAUUQmuMMXDy835fzsPbj9Gtxl+MHVSKKe+UoXfLmkwbXlqTJACwPgq2bogic1EiXgp6PammIeMegZQJ4NkEzl+Q8c+hpn2T57nnFp/Xxz9/7Gb9ks1+Yl3ZsWzaqgzZg9VLC3PxjAmXI5OxEjawdUIYs+95GhRT/fQWjlmxpjeB96fFI8380mkzExZl4/2fhvoFI/WKoQCs4VYii0QQFmnDFmHFbDXRoVcbbm1dj+mvz2VQi7eZPGhW0De8rEj7ApA+PhtSln/WRuJ2KaQlG3C7BMunr2TZtJW5GkePSvUqUFgnHdQabqFjn7Zar2P3TqTnYEa8K/ZMHCf3nQ4IHF9PVsz6A0eq0y+11+30sHvtfo7vPvmvzUuP0Eq+AKhQuyxCN1faRNV6FhDhgNC+8BEDEIaiSL2ec0iyvtpbbJJ2j8czeVhZPC4vaxf9zWuz+ufJB12xdjm+2D6W7z78gX1/H6ZcjdI8MbQztZteUUv0+Xy81XE0qYn+97H8m6LcelcqTe+1a71llcAWgAF34ViYnh9/2WcvtX+nfIy0dUIoEbmee244tuskQ9u9n1Es5XF76flBN7q88mC252XOFfe4FAZ3rM7Dz1/ins6JlKlWBXN0D7A+nO95CaUIMvx5SJvFlaYwZjAUR9gC6yDaPduK5TNWcfrguYxCJaPJwL3dW/LiZz2xZnkAtOzanAObDwc0IgfJ3GOT2f/3YZJik6nXohZOu4vHSj2f4e/ft+EgP075lc83j6b6bTmkQfrO4HJ4WPNTYTxu/98Pp93LognLeLBvu9x+LH4IIRi59A1eaz0Cr9uryV1LTe205cNOZMwdaNlOKnExJXi/T32O7DiP0WjAZDXx6ox+2erjXCsObDqsK2WtGBSO7TpF5VsqXvc5BSNk5AuAunfWonzNMhzffSqjklFRBNZwG237z0DYDoGaCObbEEphpC8maOGNHiaLRDGA6tPcQB6XN8+BxjJVS/Hq//oH3X9w8xFcOq/+TruB5V8XpWn7srky8NpJK8la5Qpo2SWe3WC5I5ezzhmfz8fQ9h8EyPB++e48ajerQd3mNYOee/djzVj82S8ZPzNHmoHvJpZk9bL6fHVoUoE0rlAiByFNdZFpX4FMAktbRHiPjAed9B4D72EwVMJsrcnE9R/w+zdrWfvDJgoXj+LBfu38HsaZafdsK1bM+oNT+8/gTHOhGBRMZiMDJj9PeFQYje670lyme9UXAwK6qk/l7Y6jmX92Rrb3IMxNcNpX6S5LAFLiU4Ps0fL9D2w6jDPNRe07agT0vgWofEtFvjszjU0/bycxJon6LetQobo3PXVX+z2SEt58zMrpoydRfQIPHhypTj586lMm/T2ayvXy+baVTyrVK8/Gn7YGKM1KVVKmWvb6Q9ebkJEvAIQQjP19OF+8+hV/frsOr8dHo3YNeGlSLyIKhQP+Pl1hKIGMeDm97V+6GJYIA6xaM4MsHNltQ/VpBqdczTJ59snnBrfTo9uaDsDpMCGi8uBDV4L1XVW1JikFyJ51B3T90m6Hh2XTVmZr5J8c9igblm4l7lwCzjQnZqsJg9HA0DkDghp4l8PFpbPxFCkdrWuw9BDWexHWe/22SelGJg4E1/r0ALcXaboFU/Q0OvRqQ4debXIc12w188na9/lrwQY2Lt1C4ZKFeOCFtlRtUCng2GDyA/HnE/F6fRizy0m3dSSq+DSKlPASc8Y/qCgUwa331NU97fjukwy7/0PSku0IIfB5VQZM7qWr4W+2mLjrkaYZ/1dTxpA5bffwLhsXz5gyvgeX8bi8LP38FwZ/cX1TP+/vfS8Lx//kZ+SNZiPla5XNk5rr9SBk5AuI8KgwXp3Rj1dzyDu/jBLRC2lpirT/ANgR1vZIpQzEPwHSBXjweMDrVpg0tBwGkwGT2cgr07P/ZXakOpgzYiG/f7MWqUpaPd6cZ0c+Tnih7Eu4azerrhsAs4YptHmmF8Kc+5aDIrw70rUG/761CiglwRgsHpE/7MkO9OyxlDLbFSZoejLTd45j9fwN7Fqzj9JVStC+ZxtdeQEpJV+9N59F45chhLYKbvfcPbw0qVe+VBhl6iRwafEX7ecNeHYiUz4IyLoBrXn3rGHf8tucv/C4vTRudyv9PnmWkhWL07Z7y2x70uZETi8sQthQin3PoIkfM/LpQ7hdIFWB0WTQ5Js/DNQN8nl9vNH2fRKzVFdPenEmNW6vkrM7w3eRzEY+/qIJRec5pPpULp6MzX6sAsDn87F3/UEcqU7qtahFkVLRfLLmfSa88AWHtx1DUQR3PtyEQVN7X5PWhVdDgRRDCSFmAR2BGCllvfRtRYD5QCXgBNBVSqnfXj6dm6EYKhhSqsi0/4H9S01y19QAETUsoMuT9J3XXu09u3B5KvPzN+XYvCKWSnXL03ng/ZSuEryLkKqqvNT0TU7sOZ3hbzaajZStVoppO8blWEG4ZtFGxvT4HJ9XU3m0Rlip0bAKH698J8+qgGral5AyXlulooJSHBE9C2HMnQhXbklNTOPxMr0DXput4RYGTu6tKy2QHxZ/9jOzhn0X0H6vdJWSfL5pdK4bcV9GvdgEZKLOHjOi5C6EyFQtKyWvtHqXg5uPZvxcFUUQVTSSLw995vcA9/l8nD18gfBCYX4Pq6cq9SPmVGAqaOGShVh4fmau531kx3EWjP2RM4fOUbd5TR577SFKlC8WcNyWX3cw8tFxAZ+XYlDo2Pc+Bkzqle11pONHZNK7gBZcjo8x8kzT2gH9ky02M8++/0SO8Zer4ejOE7zZYRTONCdCCLweHy9OfI77n9feztxON4pBuSbKmbnlmle8CiHuBlKBOZmM/BggXkr5kRBiKBAtpRyS3Tg3s5FXk0eC/Xv8VrciTGvnZ7x6kSfQKgdHdhkXIHtri7Dyxlcv0aJz0yBnXuHc0QusmP0nSbFJNL2/IU073p7vPHSppoBnl+aiMda9ZiucpZN/yei9K1WJNdxClfoVGffne1dVJJWZx8u+QPx5/TVK3eY1+XTdB3kaT71QH924BQqi5G6EuDLvg1uO8Frr9wLcUpYwC70+7EbngQ8AsGHpFib0/gKXw4XPq1KraXXemf8y0SULc2zXCfo1HOKXU68ogk83jKJ2k7z15s2Jc0cv8Gqr4Vw6q1/g17Jrc96e93K2Y0jpRsY9rtWLpH9O00aUZ/ncYjjt2j2YzEaiSxdm+s7xhEflMl6UR3xeH0+UfYHEWP9MJEuYmU/XfhBUGvt6c82lhqWUa4QQlbJs7gS0Sv/3V8BqIFsjf7Mi1USwL0Dzv2fe4USmTdN9Pc8PR/45rlvB6kh1cnTHiVwZ+TJVS9Hzg24FMh+hRILl6iWZc6LTix2o2bgaP0/7jeSEVO56pBktu95RYAYeIDmbdMPD249x5tA5ytUog8ftYeH4n1g+YxUel5e7uzSj+/DHAtsjWu4A11/46eQAmOr5GXiAE3tP6/pUXHYXB9N79h7bdZIPn/rUL9Nm38aDvNlhFF9sH0uV+pVYcH4Gc95bwP5Nh6l+e2V6jHyCIiULs2XFP/w49VccKU5aPX4n9z3bKt+aNKqq8sa9IwOaqFzGGm7JVTaMEGYo+h3SPk9r3aeE0WfCk9S8K5zFk34hNTGNOx9uTNfXOl0zAw/wzx97dNt4elxefp7xG4OmFGxzk2vBtXy/KCmlvFxvfQHIXbfimxHvSa0jksxqgFXwXJ16YmZKVy6B2WbGkeK/QrRGWClZqUSBXedGpFaT6gENtAuSyrdU5PD2Y7r7TBYTl87GU65GGd7tNIbda/ZlpNctm/Ybm37ezsw9E/wqIUXkW0j39nRpCBdgBmFCRI0MGL9cjTKBBcdorooq9TXf9uLPlgf0kfV5fJw5dJ6jO09QtUElChWLYsDn/tIFs976lsWfLc94Sziw+Qi/fvknn6wZmS/3w551B0iOT9GN7xiMCpVvqcjdXZrpnKlxfM8ppgyezd71B7BF2HiofzueentexlxaPwmtr1EHMz2C1SKoPpXkuOxjPjcK16UYSmo+IV2/kBDiBSHEViHE1tjYax9A+VcwlNMx8ABKgBTv1XBHp8bYImwombTKhRCYrSZadi24tMX/Iv0+eTZoTMPj8lClQUUObTvK7rX7/PKnvW4vCRcTWT1/g985wlgBUWwFRPQBcysIfw5R7JeAGA1orSDL1SztJ2kghMBkMdH+udYAXDwRoytvYDQZiDunv6q+dDaO7z9Z5ucGctldnNhzijWL/g7+YWRDYkxSULdcpXoVGPfne0EfHhdPxjLozrfZ8ccePC4vyXEpLBz3I+N6TsnXXAqC+i3r4PUEpjtbwy3c+XCTf2FGeedaGvmLQojSAOl/6+ZwSSmnSykbSSkbFS9e/BpO599DGIqCtT2QNeXOgggvuNc9s8XExPUfULdFLa1vp8lArabVmbh+VK7T/a4n0r0NNb43aux9qElDkN4T//aUgnLLXbX5YNlQjGZ/A2UNt/Doyx2JKhLJ4W36K31nmos96w8EbBeGoigRL6EUmY4S+SrCoJ9fLYRg7Krh3NXlDoxmI0IR3HJ3bSZuGJUR8L29bQPMtkDNFLfLQ/WG+sVOu9bs19Xvd6a5+HvZNt1zcqJO85oBbxSgfU4P9W+frRto0YSf8GRxN7ocbtYs+ptLZwObmVwPoksU4ul3H8MSZsnwmFnDLVS9tXK2byQ3EtfSXfMj0AP4KP3vpdfwWjc8otCHSKUo2OcBLjBURkS9hzDVKtDrlKpUggmrR2JPcSClvKb+ytwiPfuQzuUACOv9CFMdVMdvkJSp96vjNNK5Eoou1Bp43IA0uu9Wvj4+hW9Hfc/m5f8QVSyCR19+MKMVZImKxXWD1GabmbLVr65AJqJwOG9+PZAhc15CShlwnY592rLk819Iik3OKO6yhlvo2Pc+XX14gMgiEegVRygGhcIlsu8TEIxiZYrQeWAHf10dRbvWPd2aZ3vuoa1H8eoocJqtJk4dOEexssHqL64t3YZ2pm7zmiyb9htpSXZaPnYH93S781/NpskLBZVd8x1akLUYcBEYDiwBFgAVgJNoKZTZ6unezNk1l9F0YLw5No74/45070QmjwTv7kxbBWCB8J7gWARq1pc7AZZ7UKK/KLB5XDobx66/9hERHcHt995yTb+YPp+PHtUHEHs6zs91EhZp46sjk0i4kMipg2fZ8ccetq3cRXjhMB4Z+AD3dr+7QDKPEmOTmPfxEjYu3UJEdASPDn6Ae7q1CDq2z+vjiXJ9AnLZLTYzk7d8RMU6+Ut3VVWVnrUHcfbwFRE2IQRNH2jIyKVvBJ3PxH7T+eV/v+Pz+rudzFYTs/ZPpGTFm/NNvyAINQ35DyOlBM8W8B7X/P+mW/2+ZNIXC55tIAohDZUQ7nVakNhyT45dn4Je03sEeelR/IuhMmNByyrREe8ShVBKbsnXdbMy+915LBr3IwaTIcOHPea3dzOCldkh1TRk2nRw/AjCALZHEOHP5/hwjj0Tx0fdP2PfxkMIoWUr9Z3Qg5lDvuHM4XO4HR4yf+es4RY69GpD/0+fy34+zpXI1ImaJpCxGiLydTA15tcv/+TbUd8Tfz6RyvUr0mdsd+q10C84k1Kyau4aFn+2HHuKgzsfbsIdDzXiw26fkpqUpjW68akMntaHNk/epc3Tuw+kHUz1EUJfPC0rP0z8makvfxmwXSia26lBK/0K2TOHz9Pv9tf9m9tYzTRq14ARi9/I1bVvVM4cOseMIV+zc/VewguH8+jgB3h4QId8FdLpETLy/1GkmoiM7w6+M5pkrtACvSJ6NkIJR02ZCGkz0jN/tCpbLW6gACqi8ESENbAEPSfUxCHgXEpAemAGRvTE2AAwVEEpviLP10yOT+GvBRtJiU/l1nvqkpbsYGSXcQG55UXLRPPtqS+y/XJJ6UXGPap1c8poe2jVDF2RubladackpOL1+Igu0pAC8wAAIABJREFUUYgBdwzj8LajASvUy5gsJuYemxy0kbdq/wGS38M/r97KmlXPMb7vNr+CI0uYmTGrhlOnWWBAf9KAmaz8cnXGZ2IyGylSJpov/hnLmYPnNH2ZZtWx2Czagzr+hXSZDQWQEPUBiu2BHO/96cr9g1ah3v/CvbycjQTBgc2Hmdh/Bkf/OZGuntma3mO633Aa7Xkh5vQlXqj/quZCTc86soRZuK9HSwZO7l0g17jmefIhbkxk8nvphip9xSwBzz5kyniwtgL7LMCdJfPniiGRiYOgxLq8r+i9+wlu4AEUTW7ZcwD/3rE2yEcgeteafbzVcTSoErfLw3ejTYQXCtPVtLGnODiw6TB17giuaYNrNfhOZpmbU2sC4tkK5pzzvCOjNQGyCydiOLbrZFADD2C2Gjm87RhFOzYM2CelhNRxBBZOOSlVfA5Ou3/8wmV38+U78xjz27t+22PPxPHLzD/8lTfdXhJjkvltzl9+zTmk9CLje6Q3S8m0CEx6E2mqmWPMJFi3MfBveqJHrSbVmbp1DD6vD8Wg3HASAfnh+0+W4bK7/NJKXXYXK2b9SffhXYPGTAqKkJ78TYqUaroaZFaXiBucS7Xm4jKYO+UyCrj+yPvFTbXJ/ldLQKFxYG2Llh8eDlghojfC1jlPl/J5fYx4dBzOVCdOuwvVp+JMcxF/QU8yQPMN59RoQ7r/0VwUATucSPeOPM0vNSEte/EvwOdVKVa2SJDJpGkKpjqUr6b/8zu2K1DP/MCmw7r66y67i22/7SI1MY01izayYekW3Clr0+8/61u+RytOygYpJTUaBe8W9fCADtmefxmD0XBTGHjQZJ2DBpT3n7nm1w+t5G9aJEFX09IDam4aa8gg+f3ZI8JfQDpWEOiTNwBGiHoPxVgRCk/Qetv6YsBQHqFkL6Kmx4HNRzKySfxmrkoURfg1dQDNoNbJRpkSQBjLIrHpzF8F+zxk2FO5ll2uWLccQglurAxGA2WqlqLqrZWCTCYMr9eE0Rh4j5f+j73zDIyi+tr4b2Z7GgkQeu8gVRCkN6mCgCAiKF1RBAQVlaaCAiKgKAoiIkgVpFel9957D72GhLTtO/f9MGFhM7OhCP7V1+eLMjtzp2Tm3HOfc85zrupTGDkKajN54m/cDtomMP5qPC/nfEM1rEDVxrH0GeVDmyjku28rxAl9p7J/nX6BX4t3GpGrcI50j/83InfxnJzcc1bz/D0uD9n+giLF/zz5fykkyQCmCmhT5GSw1ARrI7R5+2mhqPs+7LmNBZEyTlU7IyGDFK4W/IR/jBS9Djmk5d195YxIpmKPZODvB1uYDWtqfYBskLHYzPT+4XVN8w0NrE1V7Xs9KDfUgOwDwmQ20fP7rql51vd2nVLF456qWpQv/hgY1Gs9e/ACs77JiCMl8HeX08CpE000naQsIWY6DG6tGScxNnh15un953A73DiSHNiTHOzdYMbnDbLaMQTXbb8ac53F3/+haWIiyxKdhr5C9687Bz3234yX3nsBU5r6AJPVRNk6Jf+SjKH/PPm/Ea6du8G+NYcIiQih0vNP398Y3QdShs8Qt1qnBlWdgA3kUKSIASBHIhzzwHdWh7YxACYI74NkeDQ1CslcBinT3D91/Q+CYhULYTBr6RBrqIXu33TCYjOzdfEuMkRH0Ljrc+R76v5pgZIcjoj8DuI7oqUsXOBYBOG9H/ga67atTuacGVnw7XISYpN4pmFZKtQrTeZcmciYTT/YegcLv13OqmnR+Lw+XnrrBmarICVRZuro3Dzfqyut++5m3ldLcdpdZMoRxZujO1C+nlYWOiprBiw2s6ab0Z2WkPfi1jUTMcdsFClj10rm2BchwvroTkoH1h/FYJTxpJkfFEVw9ez1dO/T4/awZcFOzh48T64iOajxUuU//f7/XZC/ZB4+W/whY978kRvnbyLJMrXbVKXHfZQ4Hxf+M/J/E0zqP4N5Y5ZhMMhq5ocEw1cMSD9AeB9IxvwQvRrhWKB2HzI+hWR74W77vUyzwbkU4VwLUiYwl1aDppIFyfoCkunRz/1XwWA08Mnc9xnY9AuEIvA43ZhtZsrXL0PdV6tjMBio2TqwCOfckYts+G0rQghqtKysm1IpGYsgMKERlXtIpCTaGdV5HDuW7kFKlQcuWCYfRSo8WMHXtXM38XkFs77JyuyxWbCFKdiTZEIiQqj6cgLtP2nNq4Na4XZ6sNjMQVcENVtX4ce+0zTbZVnCq8PVR2b26OvMi3hQroAhp+an8KhQ3awlg9FAZHTw4OKF45d5r/Yn2BPsuJ0erGEWJn44nbHbh/0ldMZfgXJ1SjHlxLekJNgx28yPLAD3KPgvhfJvgL1rDvFJ8xGagGB4pjDmXJn4j6ms+1/iTgpl4q0kytUpSfFni+gavFlfLGDGZ3PxpPL4JrOR1h80o/0nWopDiW2emil07zdigdA3kMN7PtB19X1uMEe2HA8o9beEWBiz+TMKlb2/TO2vXyxg2mdzcafxwE1WEzPOjX+ozIyj208ypNVotVMTEJohhHYDWzLh/amad2/ihhPkKawnhWxGit6gSnWkgdvloU2O10mKTwnYbrGZ+WH/KHIVzq45ZvaXC/l5wCwNXy3LEmVql9RkCd0LIRQ1EGz/BUSyWtsR1gvJ8O+YGB4G6aVQ/sfJ/w2wYtIa3YwPn8fHwY3H/gdX9L+D2+Vh5S/r+aT5l4x+fTwn95x5oOMiMobT9M36tBvQkhKVi+oa+MunrzJ9yG+4HG4Un4LiU3A53Mz5chHnj17U7C9FfgVSZGprRln9r6k4UtiD5TZfPXudo9tOarRcPE43c79a+kBj1GtfE6PZEBBasYSYadGzka6BT4xL4sCGI7r0SIlnizDzwni+2fw5YzZ/zswLP/D8G/Wo2qIS1lCVGpEkCUuIhcuXawNp20wawFRK18CDqp305epPyJg9Elu4lZAIG7YwK32n9NA18Ic2HWPakLm6AWFFERzYcERXHOwOROLHkDQCfDGg3ATHfMSt5qq093/w4z8X8W8Alz14Sl9awaZ/M9xON32q9+PC8Ws4U9xIssS6WZt56+uOPP96vT89/rbFu3UlcL0eH1sX7daU8UvGApBlg5qK6rsCplJgrhzQten8sUssGruCK2evU65OSZ5/ox5hkWoQ+fr5m5jMRo0XriiCy6eucj943B4GvvAFXpfPv5iQZImnqhSj6xevBuwrhOCnj6azcOwKTBYTHreXp6oU4ZN5fQP0i2RZ1tBTH0x5m/UNy7Jj+V7MNjMNOtTiqapFEAl9wblKrfgFtbtX5Ne616pWVu+jYOE9zDrZiBOHCuK0y5SoXASLTZ9bX/LDStyO4O++FKzpMCB818CxkEA6zQtKMsL+K1LYm0GP/f+G/4z83wB1XqnGvjWHNN68z6tQuqZWevafBK/HiyRJ9209KHxXWTm+F+ePKrgcqhEVisBldzO+9xRqt6n2pxuYywZZN51RkiUMRu2iNjEuifhrt8leoCHmMG264s4V+xjy0mg8Lg+KT+HwpmMsGLuC8Xu+JCpLBvKXyhNQfHQHJrORMun8XRPjklTlyk3HuHTyakBxkVAEhzcf41rMjYBWkCunrGfxuD9wOz3+Jhf71hzmxUydeKZhWd4c3UHVpU+DrYt38W33iSTGJYOAGq2epWC5/EiSASnyK4Q3BjyHwJANTBUCJjj/NQkPIv5NVR5DuAEzRfMb1epgU/DgafLtFIKxxbIsUaFBmeBUpedIkB4NTnDvBP4z8nfwH13zN0D1Vs9SqkYJrGFqup/BaMBiM9Pnx27Ywv6cYftf4WrMdfo+N5jnQ9rxfEg7BjUbQdw1fV1zIRREXDs2LU7yG/h7YTAZOLL1xEOdXwjB3jWHmDbkN5b9uIrk2ylUe1G/M5YsS1RveVc21u10M6zdN7TJ2Y2elfvTMksXfhu9OOAYRVEY1WUcrtQCLFBlcRNuJDBz6DwAMmSOoGn3BlhC7k4QskHGGmalxTtaeYD467fp+9xg2uR4g05Fe/F1twk4k7W8uMFo4NCmQBrvt68W61J+ik9h5/J99KjUj9grgfqAx3eeYljbMdy6Eo/H6cHj8rBp3naGt/vGv49kzK8G680VdQ08oPLi7l2pBVRewA4iEXG7J+nF/Gq0quynidIiU86M9J6QTtN6Qw5VqkP7AxjyBT/u/yH+8+T/BjAYDHy+5CN2/3GAbUt2ExYVSoMOtXQ9r78aG+duY9YXC4i/dptS1YvTYUgbXX71XjhSnPSq3J/E2CR/MdKuFXt5p+pAppz4VuvVu7eDEk9YRCZUXiLQ21Ylkx98svO4PfRvNIzju07jTHZiCbHwY99pjFg1iB7fdeG7HpP8nL0Qgm6jOwR4xd+8NZEtC3bgcXn8nvgvn8whS55oar6kNl+5fu4m9kRtxanX42Pbkt28/U1nhBDIBhmPy4skSwhFkCE6ghErB2l0aoQQfFBvCBePX9GtTA2AJBGZho9PiksJsrM6ttvhZuG3ywNontlfLtJQSW6nhz0rDxB7+RY3LsRy9uAFchXJTumaJYLr/Tjmotuv1neTo5v+YMH4Y7hSXNRuU5Warav4//5121Vj+cTVxBw6jzPFpa6oDDINOtehx7ed0004kEzFEcZC4D2GEF6O7Azl/EkLuQoKyjRtF/S4/4/4z8j/TSDLMhUblaNio3L/60vxY86oxUz9dI4/ZrDxt23sXLGPH/aODDCKabFh9lacdndAtanPq5AQm8jOFfuo3DRNEoByHRA07XiLHasjcDkCJ4GwDKEUq/Tgrf2WTljFsR0n/UU5d65/wPPDKVA6L/lK5iFnoWwUe7YINVpWCtApdyQ7WPfrFg3N4rK7mDV8vt/IW8OsKD59YxyaQeXAV05Zz5JxfwQEFpPjk5n6yRw+mfd+wDHHdpzi+rmb9zfwqNkq5euVDtj29HOlWDtzc9CqVo/by9HtJwO2XT51VZcuMZqN9Gs8lGtnb6gTlSyTJU9mRq8fTIbMejpGQc7p8TK250TOHFInh/3rDrNy6gaGLe+PLMuYzCZGr/+Ujb9tZ/P87YRnCue5V2uQEJvE9qV7ePq50ulSdFLGn0i5+D4ftrjOhVNmhCIhGyxkLzCeUes+9esH/X/Hf3TNf9CFy+Fi2uA5AUFhRRE4U1zMSKUjguHiicu6NIPH5dEPOJpKg/BRunIK7fpcx2xRCAnzYQvzkSm7leG/D3goSdaVU9Zrqi4BEm8lsX/dYU7uPsPWxbvZNHcbUdkiA/ZJvm0PaJ94L2Ivq3SH1+PlwLrDhEWFaTh+a4iFFr1UKmbu10s0FIrH5WXH8j2kJAZq49y8EPtAWi2SBEUrFtKshjoOaUNohhCMOp2eQKV48pcKrFYtVb140M5Ql05cxZniwmV340h2cvnUVb55K0ilr7UFetXT8dclzhy6e0/OFBdHtp5g5/J9/m0ms4m67arzyby+PNOgLP0bD2VU5+8Z2el7Wmd/nS0LdwZ7FEhyFD8Nr0TMsXCcKQZcDhlHsocLxy8ztsckDm48yoyh81j+0xpSErQrHeGLRaRMQyT/gPAcDXqefzr+M/L/QRdXTl/TDVIqPoXDm9NP6yxYJh+2MO1HbzKbNIYGVBkErM8BNl7ucZPpu4/y/jdXGTzVwfTzEx65eUV6cNldnNl/jh1L9wZsz5g9EksQnjjxVhIjO39P7+qDGN31B+Kv3b4rHWszY7aaaNCpNg061krdX19KQJJlTYPoIhUKppsueAdCwL41h7h08krA9qx5o5l46CtavPM8IREhmonKZDHyYpo4QOu+zbCGWAL+ziaLCUVRNHpAXo+PrYt3a1YaTruLnz4THN1txpEiIwQoworXY+XLdwqRlnpzJjvZuljbL+DW1XhGtB+Ly+7GnujAnujAZXcxvN03xF8PnhK5duYmTYqq1+1l/a9b6N94KFM/mc34PpNpm/ctju885d9HONchbtZBJI1EJH+DuNUGJWFgujGEfyr+M/KPCY4UJ0t+WMnnL3/FT/2mc+2cbkvbfwwyZo/C69anDrLepwqxWstnicgcHuAlGs1GshfMSrm6pXSPkTKMgvC+YChEhuhsVG31ImVf+BWj8eHbFzbsXDsg2BkMjmQnu1cGqkoaDAbe+rqjRhMG1MyWtTM3cXrfWZwpgSsVAfx0ZAw9xnbxe+TlnyutKxsQFhniV508vS+GtTM3kZJop0brKgHnDebYGwwyx3ee1mzPlD2KN758jV8vT6Beh1qYLCZkg0y+krlp+mZ9xveZwpg3f/SrVGbNG813O7+g2ouVyJA5nIzZI1EURavkcM/9e9weFny7jM4l3qFdvrfoWKQnC79dzbvN8jGkaz5mfZOVKSPysG3bl5w+rKV2DEYDERm1NMqGOVt1DaxPURjY9As+rD+EBWOX40jz3PXUHUGNQ7hSKUNnigt7ooPBrUYjhEAIByKhD2ocwYna18AJjiXg3qx/8/9g/MfJPwYkxiXx9jMfEX89AZfdhdFsYNF3v/P50n6UqanfBefvjgyZI3i2aXl2LN3jT8kDtRDnlY/SlwM2W0yM3T6cH/tOZcvCnciyTJ221egyrG1Q2kWSDEihr0KoGhg8vS+G3yfPxZHkpHrLZ6nYuFy6lI3H7UEoArPVzPNv1GP7kj0c3nIct9OTqqei9ZJNZqOGrgF4rl0NwqPCGNh0uMbgBZv4jCYDty7fInv+uxNghyEvs33ZHhzJTrxuNfhqMhtp9W5TkuKS+fTFkZzaG4NskFB8giIVCtBhyMus+Gk1jiQnUdkiiTl0XntOieDSxIAt1Mr7k7rTZ0I3EuOSea/mxywevxKX3YVskFk9bQN9J79NzdZVyFU4Ox/PeQ+fz0frbF3xBTGakiRRokoRRnYax45le3ToMIm9G8LZuyEca6iFroWFLu1lNBlo2LmOZrszxaVrsL0ur1oQJ+DI1pMsGb+S73cO92edVWxcjm2LdweNReQs4CJvESeXzli4cSWFc0cukq/QGfT9WwfCsRDJUl13rH8q/jPyjwEzh83n1uU4f6m81+3D6/Yxov1YZpwb7/fsvB4vPq8vaHHI3w0fTOnB12/8wKZ5O1JVHE28NaYTZWuXvO+xUVky8OEvD1b6nxbzv13Gz/1m4nF7UXwKG+duo1zdUnw6v6/G0N++mcDXb0xgx/K9CEVQvFJh3v3pLYatGMCRLcc5tOk44ZnCmNRvBslpyu0VIbRB4FQUq1gIo8moK2OsB6EIzLbA1cMdCmXe10vYs+oQ18/fwO10M/2zuUzqPxMEAfTH8R2nyV8yDz8fVVMYr5+/SdeSfQKMvGyQiYzO8ED1EwajgRWT1nD9/E3/RH2nyvfrbhOo0vwZTGZVQ+X2jcR0dfZDI0No82Fzhrz0lSYjJy2cKS5O7z/HF38MZECT4f5n6PP66D2hm27WWMXG5Zg5bJ5uLOXOROuyu7h+/iZLJ6zipfdeAKD7mE4c3XoCR7ITZ4oLa4gFt8uDLHsZ9NM5ylVLxuuRMBgFJ/aHIenq5Ouc7DHC7fIweeAslk9cjTPFRclqxegxtgv5SwZX9Hyc+E+75jHg1QLduX5O2+7MYjMz8fBXRGQM45u3JrJp/g4Un0KB0nnp82M3ipQP3lzh74SURDvJ8SlkzpURg1ZkXAMhfODaiHDvUKskbS8gGR5MUjX+RgKv5nsrYPUAqqrkgFl9ePae7kmKotC15LtcOXPN74FKkkRoZAhTT38XkF1x5sA5Pqw3hITYuzr6RpOB8IxhjN87UjelsWORXlw5cy1guyRJSLKk8Ryz5MnM9JhxusFTIQRdS77LpZNXgnqcd2ALs7I48a6Q2IENRxjx2lgS45JQfIJC5fIxcPa7ZMmdOd1x7uCtCh9wem+MZntIhI0RKwdRrKKateR2ummRqZOuAc+UI4qJh75i2+LdjO05STeofi8sIWY6ff4KLXs3wef1cWjTMdxOD6WqF/N74Ie3HGdc78mcPXCO8IxhtHq3KVdjbrBm+sb7NnXJli+aKafG+t9Fe5KDNTM2cWrvWQqUzovX7UUkj6TJa9ex2O7aN7dLwhz5ElLEAMSNyjqNYWxIUWORLDXSPf/D4pMWX7L7j/0B73RIuI2Jh7964L/j/fD/XrtGKHaUpK9RbtRCuVEbJelbxH27Ij04gkmiejxerCEW+jUaxub5O/Cmeqan98Xwfp1PuXnp1mO7hieJ0IgQsuaNfkAD70bEvapynvafIXkMIvY5hGvHA51r35pDQTM+Ns7dFrBt/7ojxF6+FUAxCCHwuDysmrohYN/8pfJoAslej4/EW8nM+FwriSxJEu9OfBNLiMVfDWuymAiLCqV+x5qYrCZsYRZCwm1ERkfw+dJ+QbNjTu+L4caFm/c18IBmcitT8ylmnB/PxINfMXLNxzR9swEXj1/GFyR9My3uSCykhc+r+FM9QW2Y3bBzbSxpViNmm5m3v+lMeFQY0bkzBY0T3Auj2Uj9DrUAdTVRtnZJKjYqhy3MhhAeTu09xUcNPuPUnrP4vAq3byQybchcrKEWBi/8kPodalGl2TOYrfpKjTcuxjKs7d2CrZBwG03frM+7P75J8x6NaN6zEY3bxQUYeACzRagy0ViRMnyFmhFkRZXWtoGtKZgfL1Vz5cw1jYEH1btfOHbFYz1XMPzr6RohfIi4duA9jb9nZ8pEhHszZJwd9MO0JzuY8+Uijm47Sd4SOWnyZgPyFs+lu2/T7g0Y13sySpo+nkIRbFm4i7MHz/upnDvwurwsGf8HnYe2/dP3+HeCsP+qlpz7i2NcaoOphN4QvSVo1SSAEE6yZDtB6WcT2bPeiNdzd19ZlghJUxB17vAFPE6tbIDL7va3VVMDbYLr527iSNYRgfP62L50j25D5Sx5M/NC9/oc2nQMIaBC/TI066EKg7Ub0Io9qw6SkmDnmYZl0116376RcF9ZB1AnlnJ1tFSYEIIZQ+exbtZmdRxJNd6j1w0OqFdQFIU1MzaxZPxKPC43tV+pTpNu9Ti+41SAdyzLEtkLZCF30UC54Le+6khCbBIbZm+9+3w8Ps4eukD1ls9SptZTREZnUAOaQSYsSZZo0q2+Jkdd+K4iEgaAexvTB+bB7QgMyrrsLpaMW0n7T1rzdN1SCCHo8lQfLp24rMnlV3yCHUv3qPy6Tn8Ao8lI8B40bkBBstaB6LXgXK62WLTUQDI9/vjZxeOXMVmMGiPvdXs5tefsYz+fHv71Rh7XRlWlLqApswu8J8G9FSxVNYfsXnmAAc8P87/I+9YcYukPq+g17nUadamr2b9e+5qMe2eyZrtQBPPGLNXVRfG4vcQcvvDIt/VXwefzsWnudtbO2owlxELjrnUpV0c/QwZI9ZR0lvPCCd7jYNLnkoVzLSLhXYoXl/joOyc+Hwzpko+D21RjYbKYaNCxtn//4ztPMXngLN1gnTXUQqGnCzB54CwWfrcCe6KD3MVyBE1RDNfJ9lg8/g8mvPcLQhEoisBgMlCp8dN+5ccdy/Yw8cMZGIwGpn4ymzwlcvHZ4o80tA9A0WcKaT7yOzAYDfi8Psw2M0aTAUUIulf4gLJ1StHq3SZkzBbFqqkb2DBna+oY6jjOFBefthzJhH2j/GON6jKOTXO3+w36heNXyF00B3lK5OLkLlXNU5IlorJF8tnijzTXIhtkDm8+HrDN5/Uxd/RiSlUrxtPPlWb0+sEMfeVrTuw6E7Tt4rZFu+g6/G7VqRAuxK2XUlsHKsQcs+gWYckGmcmDfuVazHXyl8zDez+9Sb9Gw3AkaVfdkixxbPtJchTMisFo0E6i5kqpmTJpTmQqq3ZNAyRDZghtr72Qx4jcxXLi0QnWG80GCj19f6npx4F/PV0jPIeCNmXGo+1FaU9y8HGzLzSeitfjY2yPn0iK1+Y+J8cla9p73UFibKJ+E1+bmeLPFnnAu/jfQFEUBjX9gtFdx7Nt8W7W/7qFj5uNYFL/GekcFYRGEF6C+RTCdw1xuzcIOxIphIT7CI/0MWRqDJmySZgtgo79kihY5HeVelMUPm0xUpe7lWSJsMhQjm0/xbwxS/3SAxePq3x4WmNgDbXQqk/TgG03L91ifJ/JuJ0ePG41WO52uJk9YiExhy+wf91hfvxgOi67C3uiHZfDzZn9MQxqOlz3/iIyhdO2f4sAnRaz1USWvNG80r8FNVo9S+Wm5fF6vOxbfZBTe2NY+O1y3ij9HrFX4lg87g/NvQpFcOnkVa7GqJLCF45fZsOcbQH7uR1uzh26wNkD5wOOS75t13SIArXZtz1R+604U1ws+WElANG5MjFm0+d8s2Vo0HfenVaUzblK1XtPrYzNW9SJJGmtvDPFyfIfV7F9yR7mjl5Cv0ZDqdu2mm6BlxBqwkPT8NdoGv4ao7uOD0ivlCIGqm0nuXONRiAEKeJT3Wt+UshRMBtP1y2F2RpIg5ksqlz0X4F/vJH3eVWtkPnfLOPA+iMovniEcCIUO8K5AnxX0e1lKlnBoNVg2bFsb9ClqBCwd/UhzfaobJEYdPKhAQqXL0j5eqUDMi8kWcIaYqHJG39ePvdJYteKfRzadCzAcDhTXMwfs4zr57WBZhXBAvkCjPrSBMKxFL3SeLMFBk08w7RdR3mx63FIHo+Ie5nT+05h1/HuADJkDueLlYNY/+tmTaaGUAQhETYsIWZCM4Rgtpp4vls9nnstMNA2ecAs3VRJt8vDxrnb+G3UYq18sE9w7shFLp64rHtdrw56iYG/9uHp50pTsGw+XunXggn7RtLh05fpP7M3e1YdxO3w+D1cj9tL8m07M4fOwxlEilqWZf89Ht58XJd69Pm0hU0el4fZXy7U7OtMcQWlL9MWbxV+Oj+ZcmhXLSaLiVovq524hBBs+G0bvess4PUauZk8PBuJ8Qa1qtka+J7c6YZ2Z8XjcXtxJDk5su0ERnOgcyBJakX2tZgbKD4Fj8vDmpmbGNLq7qoGQz6wNkZ9r2RASu3b+9eIFDNNAAAgAElEQVQnmgya8y6N33gOa6hVlYquWoyvNw4hS54n398V/uF0TezlW7xTbSBJccl43R4MRi/5ijoYMScGa4hQDblQ0NIHEmABa33NmC6HO/hrIIRuMMhkNtF2YEumDf4tQAbAEmKm02dtKFAmLzOHzmfphFW4HC4qNCjLG1++RkSm8Ee99b8E25fu0fWWZYPM3tUHdakrvMGafOjTFQCIRPTa7BmMCsXLJ92zxQXeiyiObUErhXIWyo7H6cFo1vKgABmiI/h03vvEXo6jQJl8REaeQtxqifAeBzkKj6kzG37bqjOyarQkSeLsIX2azeP2En89QcN130Gl58tT6fnymu2XT1/TXe35vD52/b6f+h1q8esXCzT3YwuzkKe4eq7ILBFBHY20UHwKMQe191C8chF8Xu1kaw2xULtNIK0pSRL9ZrzDR/U/w+vx4nF5MdtMZMkdTZsPmwPwU78ZLP7+99R3yMrV82bWLYhk/JqTDJl6lvEDc3PuhIWwyFCcdheKW3vu80cuMXzFAEZ1GUfiLVXwLiTcRlJ8ckAMzOP0cHDjMS6duqoK6LnWp1KHd56rAsKDiH8dojelGxt63DBbzbw9phNvj+nkf4f+SvyjPfmRncYReykOR5ITj8uHM0Xi7BEr00ZnBnxqQIU7Hp+EOqeZwFgCKdOvSJLWw69Qv3RQ7RKDyaARhrqD1u+/wNvfdiJb/ixYQlQq5os/BlH0mUKYzCY6DH6Z3679xOKEaXw8571/RO/KsKhQ3UwXSZYCMjMCfwzmNwTvaSlZqoP0oCqTdgqWOIHZoj2PJcRC/Y61yZY/iy5fLMsShcrlJ2+J3JSvV4bIqIuIuM7gPYzacOImlw/8gGzQp5xkGWq+VDmoMBlC5VrTIv5GAmtnbWbrol24HC7Wz97CG2Xeo2V0ZwY2HU7ctfigRUiRWTLQsk8TshfI6qd7jGYDlhALH07t6a8beKZhWcw28wNlv8gGmcLlC2i220KtvDP+dSw2s79S1xpqIX/pvNR9VZtWWOLZIgxZ/CFmmzmVBpOIvRLHzhX7ib9+mwXfLg9wEjxumduxJn6fmZGyVd1M2OjiD+9MFsRNISzI+yTJEnO/WkpKgp3wqDDa9n+RvCVyaZIcQC1uu5qa8iocs7n77d8DYQfPgfs/pCeEv9rAwz/Yk3ekODmw4YiGWnG7ZFb/lpHXB11Lc4QE1qZI4R8EbV8GkDlnJjp+9gqTB8wM8GoMRpnPl/TTcGv+0SWJRp3r0qizjnf7D0WDjrWZ/81yjQGSZZlKzz+tf5C1GTjmEeiZm8DaKPgLbqoA5hpqkNz/YVpQvbC0xtqMwZSTQXNeZWDT4Sg+JbX5s5USzxahXvtnMXKM9oNKMW3o4QDKxmQ107b/i/5/i6SxBAbkITwqBZ/Xi57/U7CkQt4SucmcKxNxV7V6KkazkdAMgWkd879ZyqR+MzGYDEiShMflQZIkv1e+c/leDqw/StEKBTieJphpDbXQ+v0XCAm3MW73CDbM2caeVQfIkiczjV9/LsBRMJlNjFr3KZ80/5LYy3HIsoTZZqbw0/k5tOlYwHMw28y07tsMj9vDxeNXiMgcTuYcagVtvddqUqhsPpZPXEP8jQSqNHuGGq2e1ZX99Xl9DG0zhpTbdv+/AUZ3GUeDLnXx6QS6XU6Z3euiaNm7Nm5jb64evUrGbJE0fuM55n21NCBWYDQbEArs/mMfQoA90cHsLxaSu3hOTGajJmPN4/KQ9062TdAUaQlE+nn4/zb8Y4uh7EkOXszcSdcDiojy8tuRI9qDrM2QI0c+0PgxBzYQd2Y4V84kYg01UqXFC4Rk64Uk3V8T5d+E1TM2Mqbbj/4MIaPJwGdL+lEiTdD4jv547qJRhIoe4D2RKg0vgSE/UsapSHJwekoIBVwrEY6FgBFszSHhYxC3uJdHTUoIZdp3ndkw9wCyJJGvZB4KlctH+XplKFPlAlLSINRVm0JyopWBr+bn5D5B4fIF6D6mE8XvkSxWbtQERauK+WHrAhzeERqQwmmx+fjo+ytUbb+KVdP28O3bEzWcf46CWZlycqx/Mju9L4be1QfqV3HeA0mSqNSkPI4kB8d2nPJX2rbt34J2A1shhMDn9T1QQ3chBBdPXMHtdKticKkByoVjV5CSaKd4pcJ0H9OJ0/tj+OHdXxBC4PX4KFm1GANn9yEio/Zv5Ha6WfDtclb+sh5JkqjfsTbNezbiwLrDfN7ma42uviRLIETQDJoGnWqTq0gOpg2egyTLeN1enm3yNG6Xh31rDmM0GfB5FWzhVrUnQRpHzmQxYrKYcCQ5/Xo3FpuZqi0q0m/6O+pzsM9FJH6GxpuXQpCy7ECS/hlV5w+K9Iqh/rFGHqBn5f6c2Hk6QNjIaFJo0CaOXiPSBMCkEKQMXyNZa3M/CN91RGzj1IyAO2NbwVITOWrsA1+fZlwhwL0d4VwOmJBCWiCZ0klH/JvAkezg4IajmKxmStcoHmBsHClOhrX9hj2rDmC2mPC4PDTv2ZAuQ0oh+U6DsQCYnn6kZarwnkPEvw2+iyDJeDyhdKtTgusXkv0er9lqosgzBRm96jWIa0Vg/EVSK26jN/jT5u6FEtcF3Js02xPjDQzulI+TB0MwmgQ+j0TbPtdo0zMJKfoPhJSVEe3HsnnBTiTUVZ7RYmL0usEBedvf9Zqk6skrAmuID59XwuPWZ0ijc2di5vkfuBpznVtX4slfKg8h4TaWTljJ1E9/I+FmAlHZoug87BUadNB/h4UQHN12khM7T5MpZ0Yqv1ABs04GzIENRxjw/LCAycdoMlCiSlFGrxsc+IwUhfdqfcKpPWf9XrbJYiRX0Ry06NWY8b2n4LhPBey9sISYaf/py0wbPCeAyjFbTdRoVZm2A14k5tAFchbOzsiO33PmwDnNGKEZQug9oRurp2/kwNrD2MKtvPB2Q175qIU/e0otyusE3iOp2XUmwAAZRiDb/pqslr8S6Rn5fyxdA9B38tv0rjYQt9ONy+7GFuojMtpLxw/TUDVSCJirgaXmA40r7NNTl3T3ToBOcK1HeC8iGR9e+lYIgUj8CJy/py4lZYTjN4StGZK5IhiLI5kKa47BvRXhXAoYkGwtkMzawN2Thi3MphswBPi2+0T2rjqgto9LpSAWfb+SnIVz0rjrS3/uxJIVTMVTvW0zW1bVIu7ahQBKw+30cHpvDEfW/cxTZdIGWoUal3HvAEsV7fBhPRFxuwicGGxEZM7B6IXnuHreQNx1I/mKOwkNV0COBjkrsiTTb/o7xBy+wOFNx4jMGkml55/WGFR7koP8JVLoM+oS+Ys7EEJix6pwxvTNTdLtwE/P6/bRKksXTBYjjbrWpUiFgiz7cRUT3p/mD+bHXY1n7Ns/YTKbqPNKtYDjPW4PA5t8wdFtJ/B5fBgtJiw2M19vHKLRipk7eolmdeH1+Di+4xTXzt0IoIH2rz3Mmf3nAmgUj8tLzMELfNv9p4eW5u06vB1/TFmnCei7nR42/LaNnt939QeucxfLwdlD5zXN1z0uDyWrFaNWa/VvemD9ERaMXc67tT6harNnaPJmfbXZSMap4FqLcK0DOSOSrRWSMd9DXe+/AU/cyEuS1BD4BrV2+CchxBePa+w8xXIy7ez3rJu1hUsnTlL4qYNUrbcXszUrWBqBJNTca2s9MFd7cG/ScxC9bA8kk1o5+whGHs9ucPzO3eWjArjAMUc14kJBWCojRY5Fksypk0L/1Io8ByAhHEsQIe2RI957+PM/AbgcLjbM2abbRem3UYto3PXR4xNCSULcehGUeO5kSBzfth1HslZ90edVOLXvJk+VCRIQVfT1yCVzWYgary7rfWfVvOrQTmpc4VZLsudNIXteOyo/b0WK+CwgKyN/yTzpVrrWebko3Qd8hzVEQY2PCirVS+SL2Wd5u0Fh/FrrEiTEJvppidmpldZnD5wPyNYCtZp3yqBZGiM/f8wyjmw57jfGHrcXZ7KTz9t8zQ97AynKmxf15TSMZiNx126TkmBn9peLuHj8MgaTQSPvewdetxfZIGO2mf0ppZYQc7r0VJ7iuYi7lqD7m2yQSI5P9neDevnD5mxbsjswnmA1UbHx0/4Ywvxvl/Fz/1n+53R6XwzLJq5m/J4v1XGs9dTv//8xnqiRl9Q18vdAPeASsEuSpMVCiMfWhiU0IoQm3eqlnuIxwVgU3LvRpP0JLzyiJyCcq9GtBIW7xVqubYjkCUjhPcGzHxzLuTspCPX/7VMQIS3/Fh6JPSn4Mj3xVlLQ3x4EwjEflBTuLa7Kkd+OxRapafZtNBvIVqgMgt1IaTlY4QFzkCAxIFmqIkX/jhBKgAEXmZch7FPAtQOMeZBCuzx02fvTVY7hSYB7hTNNZshdyE2pZ92c2B+GwWjA4/YE5OW7HW5V1CuI2uMNHSP9+8/rNAVOQgguHLtM7JU4v1EEeLpeac4fu6TJQFJ1ZBIY1nYMbqcq3SwZpHRTy4UQVKhfBsWnYE90UOvlKqyZuYkjW7SN12WDTHSezERkCiPuqrapuyXEQqZ7JJQLlc3P4AUf8G33n7h+4SYGg8xz7WvS/euOgCqc93O/mQH37Xa4ib0cx9IJKyleqQhTBv3K+WOXyF00Bx2HtKFMrccnXSCEoq767VPVFaO5OlL4u0iGbI/tHI8DT9qTrwicFkKcBZAk6VegGfA/7bUlhODmxVhkg+zv7yl8N8G5FKEkgKkMSHNUA+GHBcwVkIyPWIos2VAXM+lJ1zrBMRvCeyJcawk6Kbg2PvJk86Dw+Xz4PL6g2UQAkdERRGaJ0HiGkiw90Mck3DsRKZPBdxMsNZFC2yPJqQ2qPftIGzSr82I8v3yZHbcTf1BPNsiERYZiCK3E2SM/k7OAjNWmpN6DBUN4xwf66NLmTUuGaKTwvvBnShmUM5hM2lQ/S4iVEb93Islei0n9ZrDylw2afSRJIiwqVCOPDGqq45GtJ3iqSlH/tmCCZZKEf4XgtLtYO3MzV05fxWCQEUbZn0FmCbHQfnBrxveZEuA5C1/6dIxQBLYwKx9N6+XfVqX5M7Qv1DNgkjKYDNR7rSZ963xKYqzWAbCEWHjr644aEbzy9cow5eS32BPtWEIsAfGgU3vOYjAZIW0zcoebVVM3MPWTOf4JIOFmIgOaDGPQnPeo1Dj4pP8wEIkDwbEU/3fqXIJwbYTo5UhycL3/vxpPOk8+J3Dxnn9fSt3mhyRJb0iStFuSpN03bwaronx8OLnnDJ2K9aJTsXdoX7gnb5bry80zixA36yKSvoKUcZDYH4xPgbEk6iOygK0ZUtT3j3xeyfYCDzSn3knvkkL095fkh8gpf3i4XR6+6zmJZhHtaRr+Gl2e6s3BjfpzsiRJvDPudSwhZj8VZjAZCAm30XlYO91j7kBJmYWIex1ca8B7EFJ+RMQ2RdyhVgwFgcAJJiyDwuhFlyhYJitGkwGj2UjJasX4fEk/PntpLL2bFmTS59k4sjOEnWvC+fyNPMQldWTX7/t4s1xfmoa/yhtl3mPbkr9IztpUFt1qa+HDaHuKjNmiyF0sl26BnWyQaNKtnm6Hq+TbKXxY/zMm9J3q31anbXXdcbLkiSY6VyaSb6fwZtn3Gd9nMlsW7sLr9QESmXNnonSNEgya3YdGnesQG0QZNRjVaQ21UPmFZwK2ZcqekcnHxlCnbTUiMoeTq2h2eo7tgi3cSkJskib10WAyMGTRBzzXTl/iV5IkQjOEarKLwjOG6dYsSBLcuBCrWdm47G5+eO8X3XM8LITvGjgWE+iIqbU5wj7zsZzjceGJZtdIktQKaCiE6Jr679eASkKIHnr7P2k9+cS4JF7L/3ZASbzJLJh98AihEWm779iQIj4DayPA8Fgq5JSUXyFpKKpH70C7DjaAtSly5JcI70U1w4e0Ob1WpCwbkWRtR6PHgc9aj2b7sj24Hfd2g7IwdvuwoPzzyT1nmPPlIi6duspTVYry8gfN0i3ZFsKBuPGsTi6zGULfQA7vhfDdRMTWTy1ouwMjGAshZVpE8u0UZINMaEQIy35cxfh3f9Hw1yaLkbrtarBu1uaAD94SYuaDKT2o0aqy7vX5vD42zNnK+jlbsYXZaPx63Qfq8OVIcfL7z2vZsXQPmXNmpEXP6uTP+WZAXEFdET6LnHEioBZKdSjUQ5OhkiV3JqbFqIJjP34wjRsXYjXns9jMjN0xnPwl8+BIdtCnxsdcOX0NR7ITS4haoDRq7acUfroAP/WbzvwxyzQdsjLlzMisCz+oufsuD82jOugqe2bNG02lJk/z+8/r/B66NdRCoXL5GbFqEJvn7WD19I0YTAYada5L5RcqaCaGDoV7avT574zz/a4R5CmmXyl8B4pjKSSPUaVKDHkhvC+vl1+s0em3hJj9dJMeVvrm/OmiJOFch0h4LzUDLw3MlZEzPp7J5EHxv8yuuQzcG6XMlbrtf4I1MzZplrVFy6XoiiUhHAjHAmTbC4/t/HJoG4StPrg2qZ5AyvhU4S43YAM5DClcDapKxtyIiM8hcWBqFakE+JAiv3liBj72ShzbluzRBFI9Lg9zRi4K2umpSPmCDJz97oOfyHMCdaJLCze41kJ4L7XJSMYZiIR+qmIoElhqI2X4HEmSAqRs7UlOTYNpUDNW1CpTrUc38YPpukbe5/PRr+HnHEuV55Uk2LJwJ6981Jx2A1sFvaWURDs9Kn7EzUu3cNndSLLE+jlb+XByX6rW2wyudWq2kK01Ulh3/3FRWTKQvWBWte/qPa9hYlwyNy/eombrKlw9e50pH8/W3KPX42PH0j3kL5kHW5iN73d+wfaleziy9QRZ80ZTp201/3PaNHeHbgvE5PgUrpy5Rs5C2TFbTNRtW521MzelafloodV7TWnSrR65iuRg2+LdyLJE7VeqUfuVagxpNYoD64/4M2b2rz1M3Ver03t8t4BzhWcMBR3VC6/bG6DUKoQAz341K0ayIVmbINw7IXEIfgrPdxpuv8Oo3z+jb6P1XD9/E4PRgNft5fURrzJz2AJd3j8ic/jjqTo15AShR5EZwKCtJv5f4kkb+V1AYUmS8qMa9zbA/0xA/caFWK1olUgvrnR/DfCHhSRnVKkfQNhaIOyzwXcGTOWQbC2R5LvGSw5phrDWBtdmhPCoVI3wIBQ7kvzwDa6DQQg3OBZjds5hwIQLLJ4cyd4Nd8loxadw7vDFdEZ4SMiRqZOb3m93O+VIphJImRchlBSQjEELWIpVKoRe5Y0lxEyyjmoowLVzN1AURdNOcOui3RzbedpvsIRQs4VmDJ1Pwy51A6SEzx48z7WYGxQok5e1szZz40Ks3zgKRW0kPbLrfCpdn4Q5Uj+2cfbgeS6fuqZ5Cb0eH4u+/503vnwtVTZA1hh5g1EOEL4zGA1UbV6Rqs0r6j4LPSg+JaB5eI+xnUm8lcSuP/ZjtphwOz007lqX7Pmz8FK2rig+BUURRGQKp0j5AhzedIwDG45qROxWT91Ii17PB/RgeLF3E0Z3HRewSgRQFEG3Mu/TaegrvPjO8/dklTkBIyJ5HGqee9qVn5MMtolMPLSEc4cvkHgrmSIVCqQ2J4FJH80IEHezhFj8ujp/FpKpCMJUNLV3wr33Y0Z6wvLFD4snauSFEF5JknoAf6BazJ+FEDqlqH8NSlQuii1sVcDS+NieUHweGa0KYghSSHDP7XFAMmRRM2nS20eOQMEAif1AMqTaAh9k+ArJ+idSFF2bEPZZagaLcgWUG4SFOKhcH8pWvc38HzMzdaSq0mkwGihWsdAjnystJGM+hLEweI8SIE0s2ZBCO2n3D94Bgt0rDzD4xZEoaYy8JcRMtRcrcWD9Ed2UwaisGXQbg29dtFO3vZ3RZFA91HbVSYpPZkDjYZw/dg6DwYDL4fNTBJprlyRO7z+nqRC+gytnrun2G/C6vZw/qk6sNVo9y6R++vLONVo9q7s9LV7o3pAf3guktGSDTMGy+QIybyw2C4MXfMDNS3cqmHNgT3LQ9ak+ASsiZ7KT9+sMpu6r1XWflxCCfasPBRj5C8cv6wqx3ek7O3ngrzz1TBxFCi2/h8q780yDSBF4zyNJEvlL5Q3Y3OzthtgT7fz6xUIURUGSZV56rymt3m2qP84jQIqaqK40XRsACQxZkSKGPnpyxhPCE8+TF0IsB5Y/6fM8CKq8UIEchbJx8fhl/wdpMluYNa4u3QZtTvUGPajceAOwNHjkcwkhOHvwPF6Pj0Ll8j1Q6zzdcXzXIeF97nRY8m+/3Qei16WrwxMMStJXYP8lqL6HLVSh1Vs3WTYtM7eumTDbTLTu2+yRrj8YpKgfEPHd1LoDyah69mF9kHSauASD2+Xhs9ajNVK8skGmVZ8mdBjShj+mrOO7nj+nUQe18Oog/Qk8PCoU2SBrSunV4J+6epr0wUhe67OSslUSEcCe9eGM/Sg3SToibD6vL7iYG/h7kqaF2Wry9xvInDMT7/3cndFdxvuVJn1ehfcmvenPDrsfGr9elyPbTrBxzlZ/d6nI6AwMmqNPs0XnykR0LnXsBd8u183e8bq9JN5KwmQxaqggg9FAWNTdyfnE7jPMHb1YV1jsDlx2FwmXZkFB7XspRBDhUYO2KTiof6+2/Vvy0vsvcPtGIhmiI3Srf/8MJDkDUtQ4daUpnGrB1f9AgOx++EdXvD4sDEYDX28cwpxRi1kzYxNGo4GGXerSolcjJKMTnCtB3AZzZaQgHYweBKf3xfBJiy9JjEtGksBsMdF/Vh+ervsIEgbOFQQllJwrIPTVhxpOjQX8jG6x1z1QfBLlazmJjS3Pm6PbB7SZexyQDNFImecjvGdBiVMrftPx2PVwKEjWj+JTOHfkEpIk0bBTHdwON798MpuUBAe2cCuvfdyKpm/pT+CNutRl+cQ1Gh7fYJQpX780zpQE2vWYS4ZMHgypX0/5mkmMXniKTlWL4/Pc/cjVNntZg7aNBLWpRKUm5dm5bK//nLIsYQ210PTNu1LYddpU45kGZdm5fB8AFRuX07TYSw+yLPPhlB60G9CS4ztOEZ0rE9kLZiH2chyhETaNsNq9iL9+W1df3+dTKFAqL5vnafv3SpJE1eZ3s27S8vzB4Ez2oMaf0la5SghJYLmHsXPaZVzGzmhV7e/CZDb5Jyv1mn3sWrGfnb/vIzI6gnrta5I9/597t9X39uHe3b8S/6+MPKgl+h0+fZkOn76c5hcThLT80+M77S76Pjc4IL/ZkeTkk2YjmHJqrG57uHQhHOjn1nvTUdpLB+6dauWuSN/I28JsvD/5IyRLrYc/RxqcOXCOuV8t4fKpq5SqXoIXez/vfw6SsQDw+ANV92aNvdC9IU3erI8zxYU11KJL09xB/lJ5eXtsZ77r+bO/I5HJbGTo8gGYzCYSLs7HFurDeM+XYzRBeAYf1RqnsO2PKExmI4oiyJg9ks+X9NO9to1ztzP/m2UkxSXzbJPyNH+nMat/2YAjxUmFBmV5fcSrZMgc2Ac1PCqMuu3+XKPpXIWzkylHFAOfH8aRbSfVa/UpvPjO83Qe1lbXEy1fvyxrZm7W0DJCUajS7BnyFM/F8HbfYAnxUaNJLCUrJlOsan2stiRATff1eX337ddhMhtJSKkDaGWCfT745cvstOl1g4goH7euG5n0eXbs7rN8vuTB7t3r8dKv4VCO7zqNM9mJ0WxgzpeL6D+zN1WaPRP0uJhD55nUfyZHt58kY7Yo2vZ/UVNx/HfG/zsj/6SxbfFu3UwPRVFYPX0jLz8s7WGpAcnj0RZGGdXfHhZyxP33AcAM5genToJh54p9DHlpFB6nB0URnN4bw4pJaxi3e8Sf1tQvVb247nZrqIX6HWoFbJNl2V8unx6E9ywNXjpBnRee4dj+Agg5P6VrlMBgNHDmwDl2/jaVl3vcpRyuXTDzx69R3LpuIlOOjEw7+x2n9pwlQ3QERZ8ppGs0fx4wk4VjV/iDlddirhOVNZJJR79O16N+HHCkOGlfsAe3b6jSAndUXBeMXU6OQtl0G8FUeaECBUrl4cyBc/7EBWuohTrtqpOrSA5yFcnBb9dGotxsgcGYjEF2Ar8hYhdAxqlIptKERoYi0rHyJouJ8Ixh1HjlbbBYwD5F/UGS8Xq8DO+emx2rwlkwMRqDEXze1LoM4wHir98mKmske9ccYtmElaQkOqjVugp1X62OyXyXolk9fRPHd95tau51+/DiY0T7sfx2Y5IunXP+2CV6VR2IK8WJEJB0K5mvXv+B2Mu3aP3+46UwnxT+M/KPGQk3E3Xlj91OD/HX9TVU0oNkegphawbOxXc9d8kG1pZIpqLpH6wHc1XUIiNtJSWEqMSnFIoUNRFJ+nMcphCCMd0mBGQ0edxefLdTmDzoV/rdUyX5KDBbzQyc/S6DW45ECJUjNllMVGleMV3PLBiU5ImQPBbwYAJKlzRC2FvIRpVmG9VlHNlzGLEnyYSEK2xfFc6wbvnw+cDrkbGGKBzbN4pR6z4Nyv/evpnA/DHLAqgLj8vL7ZuJLPtx9WOPfaTFhPen+g38vXDZ3fw2erGukTcYDYxc+ykrJq1hzfRNWELMNHmjHjVeqkz89dsk3koiV/YJGM23ubvqdINwI25/yJqV/Zj31VKNJy8ZJAqUyossy1RsXI4WvRqnrl7eQ4S0VCu7JRs3L5dm19qPUZMjJHz3LGxNVhMHNxzl/LFLzBm52B97ObLlOL//vJZR6z71F1GtmbFRt9MZEhzbdlK3Snvqp3Nw2V0ByVsuu4tpQ+bSvEejdCvC/y74z8g/ZpSuWULV004DW5iVcnUeTVZYihgC1oYIx2K1VaW1OZjVrArh3olIGg3es2DMhxTWO93gpSSZIOMURPwbqYUcav49YYOQjNnVCcRU9rEUf8Vdu01CbKJmu6II9q46+KfHB3imQVmmx4xj/eytpCTYqVC/DEWfefhMIOE9D8nfEpjF4VP7ylob4HTmIObgBWIOZqBTv6sgefiyZx5czrvPyWn3cs89aQMAACAASURBVPbgOVZMWkOz7g11z3Ny91lMqamJ98LtcLPr9/1P3Mivna6VVr6DhJvB9YbMFhPNujf031dSfDL9Gn7OwY3HMJoMTN66l6jMOrSi7wLzRk3VFKqBWiOQVjztDiRjPr90R87CULJaUQ5uOKbZT5YkvD4fv36xMKC+w5ni4syB82yev4NaL6vfg8Wmb5CFEBiMMr+OWOjX3S9b6ym6jWrP8R2ndIuqJNSU7LQKn39H/GfkHzMKlM5L1eYV2bpol99rsISYKVQuPxUalHmkMSVJAktVjfEWri2I+LfwUzmeA+q/I8cgWesE7KsoCqf2nMWZ4qJYpUKYozeo+ysObt/Oj9kTQVjo46UKbGHWoFWH4Rn1z3Xp1FWWT1zFrSvxPNOwHDVbVw5YcushMjoDzXv8SY1w12r0SWMfOFdjMHZCkiAq2sOod3JTpmoSik87mbvsbtbO2BTUyGfMHqlL58myRNa8mXWOUL3/DXO2kXw7haefK02xivo00IPA4wke/CxT68GTDT59cSRHt53E6/bicXlwpxMeuhoTj56CSty12w/c87T7mM70qtxfMzmarCZQVJG6tEV8zhQnI9p/x7Htp+g09BUad30uoGjrDqyhVlZMWsuG37b6V507lu3l0KZj5CmeS7fa2OvxEZU1w32v+++A/4z8E8CHU3uyduZmlk9cjcftpd5rNWnUtc4jp1EGg0gajpardyKShgUY+XNHLtK/8VCS41OQZAlFEfT+4Q2y5cvCyI4/c+PiLYQQlK5Rgo+m9SQq66NV1ArPCVBiwfQUkhxJSLiNZ5uWZ/vSvQEfoDXEQqs+2nzlrYt2MaztGLxeVRxt66JdzPt6KWM2f4bF9qQ7+QRbuUggGTCZ4vlh3SWy5riJzytx8oCN2d/pH3NvcVFaFCyTjxwFs3H+6MWA9pImq4nmvRpr9t+z6gCftlDrALwuD7OGL6BKswp8NK1XugHkYChfrww7lu/VzGeyQb6v3tAdXI25zvGdpwNSP5dNz0Tbd65jDbl3YAOYKxKeKYSUxBuacbLkzvzAk1XBMvno8V0Xvu/5MwaTASEE1lArw1cMIOHGRZ5rdROD7GT3+nAunbmrF+T1eFnywx+c3HOG0esH06BjbVZMWoMQAsWnIIDKL1Rg1S8bAt5RIQQuh5voXBk5m0Y+2WIzU6tN1ccSPxFKIiLlF1XDSc6IFNoB6QH7Xjwo/tGdof6/QAjVGKSlUJRrxQkoJroHUtbjSJJaJflK7m7EXw/kYc1WE0hSoFKg0UDOItn56dBXQT8+Pc9L+G4i4ruC9xxIBlW9M/R15PBepCTaVa9v6wlMqZ2jmnSrx5tfdQwYx+P28FLWrqQk2APGttjMdBr6Ci17N0n3Gf1ZCN9lxM2GaItuLJBpKdx+C+GLQUp93kLAaxWLc/NyIAVgDbXQd/LbQXVxAG5djWdwq1Gc2ReDwWjAYDLQZ0I3zTHBnok11MIHU3pQveWDFULdi6tnrzFveCeadrhEeKSX/VvCmDY6Nz3HD37gFN8jW08w4PlhAddlMAoGTTxH+ZopmO9MyHJWpIzT2TD3JKO6jAs0lCEW+vzYjbptHy5byJ7k4MiW41hDrZSoUgTZuw0lvjtOuxtZVsvXl/ySiYlDcuDX6wesIYIRK3tRokoNZgydx/TP5uL1eEGoGkdej0931flU1aI069GI8b0nk5xgRwLqd6xN9zEd77vCvB/UngnNwHeDuynNNgh7GznsjYca61/bGerfDuG7hkj8JLXBNQhLTaSIwUiG1LxeOTMo17UHSlH+CWHf2sO6TRw8Lo+musTn9XHzQixHtp6gZNVid/d1e5gy6FeW/LASZ7KLwuUL0PO7Lv/H3lmHyVFlbfx3qr17XCJECK4LQQIkSHCHLO6y6GLBJYuTRYK7LRrcIWhCgi0QXD7cWQiWScbbu+t8f9wa6enq8QkB5n2eeSDdJbe6q0/de8573pcV1zFOVlp/tKMv044mF70V9a1IpGRLLpl9Nj9/8yvzf1jAmFVHUVadv8z9+v3vXV2GkvEUs+58acCDvHhGoMWnOwJy7VB8MkIjav/cGuDBfHTn3vk9p+2xEpmU32n3t9l8v4ldBt/K4eVc/dr5zP9xAdH6KKNXGtlqW9ceH7/6uetnkogmmXnHi70K8kOr7uKIqT8hzsNsox0amTjpe6xq91SRG5b622gXLXrh34cvx8FTx7HLUWPAOxJ84xARNt6jmmwmy40nTqehppHSqmIOv2z/Hgd4gHBxiHFbrwGAagKtPxohQahdv9l2+y3krdmlfPh6Wx+Bqs23b0xlzGqPcd+Fj+WM303TB8zqZswqo9hkj/WZuNt4GmoaiZSG+63YqrH7jMx2Ts9KHJqvQcN7It1mwnWOgZYaHkQvoZpCF+7uBPis+Uu+jC7c3WjNAESOpIWH3IYQtJsFNNU2u1LXVHHPlwvM/1+u5POlB13PE9c+12qc/OU733DyZucy78uf0ezPkO4gTwBAHI3e0fqvJZYZxthNVnUN8ADBsD+vy7QF3330A9dOvrXHVnM9hRXZE6l+Hik+2fxVz8KK7O/MtPJ/KsusHOfej8KcetcxHHnVP7j5/y7j2OsP7XYKYsioKpb625KuAR4Ky/t29V4hqN0AselIuxSfZSlCAo3e0u3jhItD7HPGLgQjbWkpj89DpDTMZvsfhIR3RvzrtI6x9tc6bjvDuDcpJg1y59kPUufC8ukRkq/j9r0EQsoWu9fmvGZZMGKpKF+9OcNVRgLyP1NfwMcux2/v7G9RPrSsf9k0yZdwlWsQP6Q/6rfTDAb5xQiqiibnYjecYwys7Xpyg2cW7Aaj1AhIeE8omgxSBARAIlB0OBI+qHWPv220kmu3otfvxevPDy7ZdJbl1mprTlr4Sx3/ffTNvA7QVCLNAxc/YcYjBRaEBWz33DBm1dFUDC93bV1XW5l5+4u8eN+r3T5ebyGeYca8JLI/4jHaPZqd7y4pSxBf8UZM2HEc2xy8GSOWHd6vY1ll/RVcg3kwEmDLA7s2pM9D5msTQPLfcJzQuo+9puzMqdOPYeUJK7DEssPY7rAtuPH9S1zrOVcfdQsLf6o1mlEK8eYENT8u5Ibjb+/5NeTAPVVpWeDxtk0YvD6boaNSrLZelKLiWMHJRNXICvxBHx6vxZIrj+Si505v9ZsdEHiG0j6l1IYMWD2XKymEwXTNYgJVRRtOgeSsVk9Xd7ZHAjLfAWbmIUUHo5EDTEC1SvO47VVLVLDridvz2FXPtGP7BBi90gjqfq2nfn5Dq2hUIOxn3e3WyrmxO2p1t8DO2nzz4ffgXQb3uYIfOjB8OoOIMHXGaZww8SwaavJpl4loksevfZZNe7HE7ws0+TI0XejyjtVqDg2GvfTBCx/z6dwvqRhezsTdxxMp6ZtSqM/v46yHTuTsnS5GVUknnT6ASeNyJAO6Dc8SBTqdBTxjeny4DXZalw12WrfTbVSVN558N6fIDCY1+Npjb/X4nDnwj3dVM7VtH998EjGrFEtZb8tGjp02DxEPS62xCdWjPuanL3/GbreSDYYDnHTrkay+8SqkkmlCERezl36GhA9AE3PIJU94wDPaWJD2EwaD/OKC1BvtAjwU7AGXEHiXy31JvOApnFM96N9787cNVuLJG2cRa4wzcfcJbHXgxkQb49x17oO89thbRivliK3YqQPDY96XP7s2dwEsM3ZJYzpecg40nI7JLdpAAKxyJHKw636qaTR2D8QeADIQ3B6JHMLoFUcwbeaZTF7/dFd/044FyEUBbboadxtGgcqHEKuIVDLNaVtO5av3vyMRTRAMB7j55Olc+sI5LLtG3xQJ19x8Ne75/gZefvB1muqirLXFar3qAwAQz3DUvx6k5pKbBw4gRYf2aZxu0Mz/0ORcNtiujrkzi0gl+jdxIFYRWnoRNJyKue8yQAArtBmHnvMVB576KZaVadUYAoXkTM5/egpTtv43C3+px7KETCrDvmftypqbrwZAqED6rL8h/rFoydnQNBWwzAPLuwxSfkO/Cp0NsmsWE9gN50C8K9swL3hGIlXPmMC+CHD6dhfw1rPvu7435Z7JbLqXmVlr+iNDBcv+bPxaw3sVLBzZdYdDci5twTNgGrkqH8W2LXYffmieD6gv4GP3UyZx4LkdNYcGFvZv6xjRujwEkOo5iGcID146g+lnP5CX0lpi2WHc8cXV/a5MqGqb9JFEEOlZQFI7ijaeacT4ELDKkJLzkGAv0j8Fx6do0/nOQ9wiGc+QTtlM2XNpvvzQrG48Pg8TdxvPlLuP7fP57Mw8aPgXpN8BFKQUwvtC9DryUzoBpGoGeMbw5Tvf0LiwiRXXXa5HYm/9DdWEMdKxSk0TWC8wyK75I0CCmLRHx9SI4wolPghshZSctsgCPFCw2BmMBKhsp0Muvr8hZZd2fbz0x5B8g9zZcRKyP0JyNp7gNpx821H8e8/LyaQyZDPG1KJyiXJ2PX5gGTau8C4PaZe0gvjBMWuedceLeQEeYMFPC3ns6meYc88rNNQ0sfZWq7PPGbvmqCL2FHbsEWi+BOwmED8aOQiJHNXtDmWxIkjZ5agdQ+1mowba3/K4yRch9hAtRcVAyPydN/179llzZfyhIGVDSjniigN7fYrm+ijP3DKb9+d8xLCRvzFp/88YvZyTutFa49VcqLktOQeJHNLrFVF/QyQI/t41SnYHg0F+MYGEJjkGwB1SA+JDql/LcYxalNjygI356L+f5XUJen1eVpnQi7xh+kPyH2SAxtDk20hwG9bbfi1uePdiZlw/k/n/W8DaW63O5vtPXCR50o6Q4hPR2gPI/V5CUHRs68O2UJDMprPc+q97W1NPz932Iv995E3+89FlVAzroRopoImZ0Hhu21g0Dc23oIAUdW4+04JkPMl/Tr2bmbebB9Oq66/IMdcdUtC/tzfQeL6KJEBxuYfjrxtPuGp9xu+wVp4xd3dRN7+BI9c6hcbaZlLxFJZHef7e0Zx5y/eM26RlBVjAeQyLgXB8W5wxyK5ZTCC+laD4BMAPEjZMGQkhZdf+bgEeYKPdxrPOtmsSjAQQSwiE/ATCAc588ITe/UitIQXYOAHjm+lg1AojOOqqgzj38VPY4YitfpcADyD+NZCKW8G3OhAyRbGScw210sGWB27saq+Xzdo5tYVsJku8Kc7Dlz/Vq7Fos1t9IA7R29BCdoodcM7Ol/DsLXNIRJOorXz03884boMzWPBTvntWr6HuLk5en5ctD5jAhjuv2+sAD3Dv+Y9QP7+h9bO1s0IybnH58aOwC3uSOBAIbtnVRn8qDM7kFyNYkQPR4HaQ/K9JBwQ2/l0DPBh+8Bn3H89nb3zJe7M/oriiiIm7jy/Id+8SgYnmIaZxcmb04kVC/eO/2d8Q/zik8qGC7086ehvefPo9vnjnG1LxVKsQlkKeBns6leGDF3rJgc7+4v66pkBjIIWbZ2zb5s6zH+SdWR/mZTHSiTRPXPccB3dT1qArSGhHNPU++bN5Bf8afT7+3CffcbURjDZZ/PqDnyXGtDxYXdKf4QMQzwDSIhdDDAb5xQziqYbwzr/3MHIgIqw8fgVWHt93WpeIHyruResnG+VMBDxVSOkVvbIybEFjbRM+v5dQUdea8f0Nf8DHJXPO5sOXPmmlUK4wbhmOXjffMEQEhvXWici7HKRdiuBS5PRKFMaV/7yZ56e/7JqmTqcyfP3+d70bkxuCO0D8CZOa0xjGhNsDJdMKmrH3BIWoqXZWCBe1BHUPrhebfAE4qc9j6A40870jfCcQ2BLxjlok5+2IwSA/iEUO8S6JVD1hrAg1bRhDvSz+ff7WV1x60PX89PWvAKy1xWqcdNuRvV9p9BIiwthNVmXsJqu2vrbSesvxyWtf5LTQ+0N+djtpx96dxLeOS5D3Q/FJnRZea+YtZPZdr7h6yYJhLi23Zv+5c4n4oPx206GdfNnQaUM79ynIqWYh/S5ojF1P2ISrj7wvx9vX4xVWGmdTVh10iuVf4OqZkP0etRsQa2DvD7v5ZsebwHnoNF2JFp+K1UO7zv7AIIVyEAOGeDTBkzfM5KUHXicYCTDpyK3ZaLfx/cbmqJm3kINXPs50Ujrw+DyMXnEEN31waZ/Po3Yz2AvBM9ysQHqI5vooF+13Ne/N/j88Xg/+oI/J1x3KxN0n9HwsmR/QBduR3wbvQ4a8hliFlUPffPpdLtjnKmKN7nrA4ZIQt356JVXt2FLdRTab7Xd11fZQuxFtONkwdgAzQ/fwwlObcPnkha32hUssM4yLZp1J+RATvO35E8F2S2/5kKHvIDJwKz7NfIsumISb2J1Uz0QKmI/3BYMUykEscqSSaY7b4AzmfflLa4Hsq3e/5aP/fsbR17g3SfUUT980yygJtkM2neXX7+bz2RtfdppeUlXIfuM0oCyfMxNWTaONUyH+mFHVRNDQLohnSfAMgcAm3Qr6RWUR/v3kFBoWNNJcH2XYmCEFdWq6giaewrWNX3yQmA3hXQvuO2TJalcNe4CqERVcNPOMTgP8t//3P155aC6qyka7jWeZ1cfwwv2vcsupd1Pz40LKhpS2mqP3Jx1TVdGF+0D2i3avGh2nTbd/ibW2uZnP3vVSPbKSZddYKvfc4b2g+XpyC9U+U+cawAAPQGIm7pILar6rdkX7RYHBIP8XRTya4Ilrn+PDF2YRCPpYf5ed2GzfjXqlUe6Glx98nZ+//jWHXZKIJnn21jnseuIOXfq7anIuGr0RsvPAtxZSdBTiXTJnmx+/+NldQVDg1+9rCgZ5TX+O1h8F2QWtdoeUXYn4jVSANl0E8ceBZFtaNzYdxWP6GSQIFfci3u51s5ZWleSZcvcYGsM1cGjWea8wllp1NEuvNoav3vs2J2UTCAe4ePZZneqz3DX1IR646HHSzn6PXPGU4xHwbqu6af38Bv5zyt3YWZu/H5Ovid9rpN6C7PeF3qS0aBYTdjzb9V2JHIKmPzUrAPEBWfAsi5Se77r9osOiz5wMUij/gkin0lyy7zGMX/8szrpxDqde+RwrLDOZ6WdM7bdzvP3cB65+mh6vh49f/bzTfe3YE2jdP037ffZHSMxAF/4dzXybs90q66/oatBhZ2yWGTvG9diqcbR2P3Nc4iZA2jVo3aFodqFR+Iw9hLuUQRY0CnYtWt89Xnp/QQKbAQVopIGNu9z//KenMG7rsXj9XnwBH8PGDGHqjFM7DfDzvvyZ+y98nGQ8ZaSUszbJeIqXH5qbJ1+diCW567yH+lcpNPsNrj0VAGiBTmQDES9W+dVQehF4lwZrGPjXMTWggUZwS9y5+ALBzQf+/B0wGOT/gnj9sZc47qJXGbF0gkBICYSUJZZKsPMB9/PTl1/3yzmqRlTg8eXf6CLSqW2aahaaLiCXfmeDxtGmK3O23eofm1BUFs5JgQRCftbeeixLrjTS5dhxtPHSdvpA7d/MGg9djVI4sLRuDJn/GZnlRQXfWAhta7SLACNgF4LIQYi360am4vIiznv8VB6puY27v7uO6d9c26Xn8GuPv42ddVs9uG/fXB8jlXATQOslPEtTONngRwJbdLq7HZsBDVOMbG/2O7MaW7C9KfgPIMS7DBQdAwQwzCK/+f/iU34X+uZgkP8LIlrzOJZHaZ+ZMfKsysLv7++Xc2x32OZ4OwR5ESFUHGTspqsW2Auw57sHYWxIzsKu2RK7+WZUU0RKwlz39jS22G8jSqqKGTK6in3O2IUz7j8+b29NfYjO3wDiD5ArztWCpDm3lEK3zBrEVQGxP6CZH7AbpmIv3Bu78QI0+7NRHC25ACm7HkK7QmgPpOJ2rOL8a+0M4eIQFcPKu5U79/o8iFv6rsCuJZXF/au37l8XvEviGqZ8q0CwcJBXTUPTeZgVWctTKQ3ahDbf2H9jLACr6DCk6kmk+HjzV/UMVmS/AT+vGwZz8n9BDB1lEwjlz1YDQZuyqk4cmXuAEcsO5/T7jufiA6/FzhjXpKoRlUydcWrnbAwpoXDe0jY52uZr0eQctOwWKoeXc+KtR3JiJ2NRzaL1/wRtKryRhB2jC8s4RDVMwT1l48CqBE//8541/TFau68jCZyB9Ado/CGoeADxLe9q6A7w4xc/8dYz7xOMBNhwl/UoqSzu81g23GVdbjs9XzTP6/NiiZBq54kaCAc46Py9XB8exljmC/COQrzd14sREai424idxZ8C0iDlUPRPJLxvnqx2DrI/4C5tkIHUf7s9hr5AvGPAe8giOVen4xikUP71UPvDswTSxxOK5Ab6eMwiOOw/eML9p9meSWf45sP/EYwEGL3iiG7NIO36UyDxLK6uOTnwQmALKJ4CyZmQ+QK8qyChSTmdwpp613jQqgtvGoAg+FZGKu5pVXXU5Oto83UmWGjUmbUnzLbiQcpvR/xju/MR9Aj2gl0g49IR610VKT4OfGsgVm4Av/mU6cy4bia2bePxeFCUMx84gXW3W6vP43n21jlce8ytiGW+N7WVo64+iIph5dx8yl38/PWvVI+s4IDz9mTzfTfK2Vc1izb8CxLPmOKnZsC3KlJ+c486uTXzP0OjTH9sXvCtjpRe3CnvXrML0ZqJuK7afGOxKh/s9vldj283G1KAZ4l+s+nrCzqjUA4G+b8gVJXGbybh935JIGgCfTJhgXcVgiMe7n9VwkLjyC4wtnOpV43pc+RgJDDBeHc2TIHE87TphBeCDzPz92Hy+CGwwkjlI618ZE2+gdYfWcDdKQLFxyLhvQvSIlUzkJyDpt4BawkkPAmxes4p7wqqNvrbShT2EigyhcPik1u1cz58+RPO2O7CnMYgMCqhD/56S79o/tT+WsfcGe+gCuN3XJvK4d0TV7Obb4Xmq8hdEfkhsBlW+VXdOoZqHJ2/iVNkbZmUOIYt1S91SmW1aw82Pg20L7aGkLKLkeBWLufKQuJpNPYIoEh4ZwjukCPnrGqjTRdD7B6jwaQZCO2ElJy1SNVhO2KQJz+IHIgIJcs8hN10B6nGBxGBQNXuSOTARRjga9CFO4LdiPkRfomm3nG6AvdGyq5A7To0Oh2it1I4ddLyA255EMTBTqKN/0bKrzcv+dfAPXCGkNKzkNBOnY5VxAvBrVwDQ/9CMAyaAimzlodU02Wob1XEvyaz736FZDx/xWNZFu/O+rBL56buoGJYOdsd1nmR0xWxu8j/3lKQnI1qwkjsdoXEc6AJcovhphBPYhaECstPS9nlaN3RkP7AWUmkTarHNcArWn8cpF5prQlpw4fmHGXXtf4uNHobxO7D0Gudzz3+OGqVIcUndH09vwMGC69/UYgE8JQcTnDkHAIj5mAVHd4vuiLdhUZvaRfgWxCH5ouNiQIgVjkS+UdhD9mCsB0DdAORAFJ6CSaAOnlcCYN/TaOzstggBb6/UbCy2YqEcdbC0EVdF+NCQS/TjtDka9i1/8Cu2Q678SI0u6BHoy58YLeVU+tJu3eIzI+ASx+Axk26pBOIVYpVeRdS9SxSfgsyZC5W0RHuG6f/z9wzOUX/OKReg/R7bS/FbiP/IZyA2F0DbjTfW/QpyIvIbiLyiYjYIrJ2h/emiMjXIvKFiAz0FGgQfzQk/0tugG+BBZlvWv8lVglScTd4lqJHOuAdinIS3BypfhYiR0BoX6TsKqT81t91id0eqil04Z4m2HTZMKNg1wKwyV4bEIzkP5wz6SxrbbFal+e1o3ejdUeaYJb9ygSrhTug2ZpeXEUHBDbENcR4RnZbO0Z8q5gHct4bpo7SrWN4RyL+NTuvA+RZIjrQpJPycWA3uO9fqFltMUBfZ/IfAzsDr7R/UURWBvYEVgG2Bq6XnvqUDeLPDU+BjldNtzouqSbR5BugCaTqGai4B4iQe9t6yL+NLfCubhqb2kE8I7CKj8YqPQsJTOy2m9IiQfwpyH5Lp4yeVoQgYOZNa22xGhvvsT6BcAARwev34g/5OfHWI4iURjo9imoCmi8ld2aaBrvRpCX6CCk+yWFLteTNvcYjofTf3T9IYCJ4RtK6AgNzPM8Y8G/Q5zG2wioz8t558Jv3WuBbxX1/z9KLzYShI/o0KlX9DFydcSYB96tqEvhORL4G1gHm9uV8g/jzQCIHu2iOe8C7OuIZjh2fCY2nYVIXaoJD+c1Q9QTafBWk3jQ0xvC+Jveb+Z62AGlD5gN0wdZoxUNI5lM0/jigSGgS+Dca0NqDZn5wxlfiaKV0nQbT5OwC/QF+zMw+4/w3BN4lkbDR3hcRTrzlCLY7bHPeeOpdQkUhNtlzAkNGV3c90MxXuM/z0k6669Suj9EJxDMCqp41qaX0u+BZBokckCdP0ekxxAsV95vvPPEUpmt0R6TomB49pNWuBbyFmTDBbaDpIrcBQLBNqkGKT0dr98cwv2wzHgJIyVndHsuixkA9ekYA7dY4zHNey4OIHAYcBjB6dP9ZkA1i8YYENkQjB0P02navKpDETn8FDSeTM6vVKFp7IDLkNayyy4yZdeo1yHyJRiZD8w2Q/ZjWVIfGIJuC2r1Q+7e2YlpiDoS2Q0ov6PdrMgbWF0DsfsACsQAvVNyO+DppAAPzwHIzuRAfFJ1gJIbtOqNLHt4pr2i54jrLseI6y/VswFZl4TZ/Ty817ztAPJVI8eS+HcMqQkpOh5LTe7yvpj9FG06BzHeAohI2lFgJQWg3pPgEU7OxSqH8P6ZQ25q28SHl1yJWG5tI/KtD5YOGXpv5DLzLIUVHIL7Ou4d/T3QZ5EVkNjDM5a3TVfWJvg5AVW8GbgZDoezr8QbxB0LqVXIDmw2Zz6HxbNxpk1lIvoT6J6C1e5vCmyZBAgU48BkXgas4xJ9Cw3t3HXh7iuRLEHuQVn5/y/Om7jCo/i+dZSwlvBcaf4LcdI0RTzP0zv7vlhTPEqhvrFNY7EAzjPSPUuiihGoWjT0A8ftNM1lgU4jfl3tvaKPz32aI3YtmvzMrRIwDGENec/j4Cr6/uaZgxLcCUn71Irii/kGXQV5Ve6OoCfCNbQAAIABJREFU8xPQvlNhpPPaIAYBgNr1kP6EfJ2YJGQ+xTXIqw12g+EpZ76lNTD1WF4gZQq//RzkNXY/rvRHjZuCaifWd+JbGS05GxrPc+SNbZAypOKWTh8OfYWUX4PWHdNGM8SGopNdu2oLQZMvodG7DFsquDUS3gux3N2bBhJafzwkX6b1O4h9T+c6RElIzkUz37Uqiop4YQCa3H5PDFS6ZgZwr4hcDiwBLAe8NUDnGsQfEp39+HxAmHzqnA3+9ZzcaV/UBH3ujI0+ozAtUKN3oHWHA2mTpy8+FfHkLpCt8C5ocBsn4EbAt9qA9y2IVYZU3oVmfzEGKd5lu8dfd2A3XWVohS31hOYv0MSjUPlIj44DptHI6AcVI1bnReO8fdNfmJVUzkqoGxRS8UHma+imbPQfEX2lUO4kIvOA8cDTIjITQFU/AR4EPgWeA45S1cWTXzSI3wViVYB3WfI54X4I7ezwxdubO4QgvKejuNjXrF5uMa2/IMEdyB2zA41Dco7p2tQoJJ5DF+6Cnf4KTb1vWuRbjmGFkcAExL/6ImtMAxDPcMS3ao8Cs2YXQvSWDgXjBGTmobHHenR+O/4cWrM+WrMFOn9d7PqTUNdCdAG4ed92B5oxUsR/YvSVXfMY4Pptqur5wO+t0D+IxRhSdpnhhmsaiJvZtWcEUnSMybPHn0QTM0CCSGiPNt30wBYO06J9msalaJlzshaOdBYpvdwYprtAk3PR2H2gTUhwGwj9vfvWf6EdjdlI5iOHN+11xqXkcrCzZta8cBIqQdA0WnQYEjm6HywLmxxmjA2BDXKKhv2O9PtOJ2nHFUzcmHVE9uryEKppNPUBNJxCziw8MRPVJFJ+TffGYg01aa4eP/9TaGImRI5YpA/VRYnFk9g5iMUKqgqJx9DoHSbvGphonJoKcd27CfEuC9UvQeIpNPMj4l8NApu2qQuGdzb6IR33KzkNTb9jGoI0BoTBCoE1xDAecrcG33ikxXLNv17BfLHdfB0030xLTlfT70H8Iai4p7CuTeYbk9+XkJE+qLjDFIeTLxm+v4QdG7qO6SXb/LV0hUZvMQ1fnbTpdwU7/jw0nOgEOwWyaMk51DduSs28WkatsATh4n60vrPKcY+qFhR4iLZAM9+iDWc4RV91OU4Ski+i2QWIp6rrsQQ2MJ+1xulWmqYVNjTfhErIdFd3NmY75jRGCQTG9zgd9XthUKDsDwrVFCRmoZmvjElBcKsBkyWwGy90aIEty2cvWGVI1dO9nilGG2PU/lLHkNFVBEI9H3fr9ac/M9cf2gYy36G1+xhmBWnADxJAKh8023R2vGwNWrMJ+V2PIaR0KnjHGNOSzOdGYjhyjJGsjd2HKZKaWaSUX4ME2tQYNfU2WncoXVn0AeBdCauqd4Q1tWvR+RvTfjaciAnTjl6Kt18sxxfwkU1n2ePUv7Pvmbv2y6xV1UZrNnMMs9sH1iBSeT9SoCNV7Xq0ZnOM9HMn8UeKkIq7TNdrd8aT+R9af7TTMyGYe0BoW/EFMF2pbpaRFVhD38h/3YEdnwWNJ9PWda1I2dVIoP8UW/uCQYGyPxmMuNduTo43Zri/TZdC5YNmdpX51qj09QPXWbMLjeJeTvDLgN2Exu5Bio7ufH/NAtLauJJJZ7hu8m3MvPMlvF4Ptq3sedrf2ef0Xdy1yDVtUhtWRc5sWsQPoe2R9jNf38pQ9bRhemQ+N7K24f26t+JIv2M6HrVjkI+jsYcg/SGtAdReAPWHYwKIs30LXbJ+MgyZ22YW7VvbdGxmvqVzNU1apQp6hcTzdKxvXHnyKN5+MUI6mWn1wn3w4idYYplhbLZP34OTiAUVd5qHmP0rJgAqFJ9TMMADaOxR53PuYoKpGfCMMQ90e4FzDxSePYt3SaTqSTT7E2gKlXKIXmOkjvGYWk/05gLnqkVVC+jh/woNJ9GxG1nrjoYhLw1sSqwfMBjk/4DQxqmGhdASNDQGmkRrDwX7R1pci9Q/Dim7sm9615nPHB56x+Bn6GcUCPKa/cksx1NzAQsNbIKUnMstU57m+bteJp1Ik3ZSGA9c9DiVw8vZ5uDN2vZXRWO3QfN15scugoYPRIqO7bTTUTxLICW96NQsaFbSoqXTUW6gELvHguTrEDTXYowv7kIbzjLFV7JmmzydEw/0gLaYB03mHDMetXj16VLSqdzPKhFLcv+0x/olyAOmEF71nOme1SbzYO1qRZn9ii7lGyQE4UPQ+P3QfK2hz6JoeC+k+JTOew4ciz0BKDnT/DmwE7Mh62Jx6Vmy8Oom8TSuKSABEjMhvGfn1/I7YzES7xhEt5F8gfxZYRayXzgBPwokIfWmkU/tC6yhBboiLShg2qB2zKw0UnNp1YNPvoi9cE+evXmWqwn0fRfm1u81/jA0Xe3krBMm1xq9A4323LpN7VqjKZ/5sfBG/nVp80/NeYNOHaVckRvAxSrHKr8GGfohMvQjKL0co4jZElS8JjVR1Adz8MBE2s/ko40WhZ6FNfNqmbr7ZZyyxXk8ddPzffZlFRHEtzziX6t7KUPvqriykPCYh613eaRkKlgjoPlq536OY9QeHYmD3o615F/kG6IHkeJ/FdxH7SiuD3XNFGjCW7wwGOT/YLDjs3D3KHVDGlJvo9nfen0+8S0H3uXJX/T5kfCB7jslngU7Ru7sJwP2AlZf3z0lUT+/g7pf83XkNxbFIXprtyVdVW3sxvPQ+Ruh9UehC7bFrj3I+dHmQsSLlN/hsDQiDhsnBCVn96zFXzPgn+D6logPET9WaBukYjoENgfvShDe1/iB9sHkWbxLQuRgM2YsKoZmCRe7FyDjTXFeefgN3p/zETeeeCfHbXhmjpVff0E1azwBOjSrSWgSWB2F5vzG8WnI21hVTyGhHSF2Pfl6PnFjyN1LRrYENkDK/wO+NUBKzTnLb0SCm3Syz0bkPxgAPODfyOX1xQuD6Zo/EDTxopMb7AHEa3LafcjPS8V/0PoTIfUW4AErgpScj/hWdB9n5hvcNMCFNMutLsydmb/PiussjardloqxC2iaa8usqmtao8buhtgjQKot3ZR6C208Cym7LH98vuWh+mWTf9eoY7UXwcaCpnM6BJwgeEdD5kdM6sGhS5Ze1C1rO/GPRfzXdbldT2AVH4cGNkHjT2CR4eirluWSQ2aSiqdQBctjYWftHJ35ZCzJj5//xIv3vcpWBxYOdN2Bpj5Em53iNCGjtUMaxIdGDkUcmqJYRVD5KNp4AaReBnyGqlp0Qm7KxC4gd6xJ0IR5GPcCElgXCTxQ+DqyNSCeNvcv3+oQ3MpYTLbcAxKC4C5mErSYYzDI/4GgzZfTPSnaDuiCWdIVxCpHKm4zSn52s9ED7ywv7lvREYLqEOjFzxpb7cID17xAMm6CbllllsnTfmLCNh+jvz2I+icgpeeBdznIfJJ/cGt493nrsTvJXw2kHA72+a5FPBErT35AQn9HtTYnN0x4byg6Ccl8jCZfNBozoR0Qz/DujW2AIP7VjYgWMHFPqBq9Lvdf9Bg/f/sb1SMr+eT1L0g0595DiWiS1x5/q09BXlPvobUH4np/agqiN6F4kaLDzDg9w7rWf/GulGvY0QKrekA6ljX9iZnMZOcBaty3yi43K6zSaZDcBk08AXiMm5i/DzWURYhBCuUfCPZvq7ssXztDCIpPwYrsM2BjcoNqEq3ZKrc4jA+8SyGVM/j41S+4e+pD/PzNL1z26DtUDmlGpGX5bfw7KbkI6o8mN2gEofQyrFD3rOjs38aBupk8+JAhr/aYFdFdlsfiig9f+oQzJ00j3pR7D1mWsOU/NuHE/7S5JqlmIPUukAH/WoCae0/KXQuU9sLdjRxDZ5ASZMjbBQucqjYkZxr2DWpSKtGbyL8HLsYKbd2ta+4u1K41dNCcHLuAFEN4H8Q3FgIbDaiOUF8wSKH8s8AzxqXZJ28j8CxhZtuRQ34XHq9IACofRpsuhORswILgdhA5HDTK3zZciWmzzjLCVvVvQE5+1QY7htjzoeIOtPkKSH9pNNSLju2RcBa+dSH1PHmsGc9QkDLXXTq/Lr/5bP+gWHXDFQmXhEg0x3MsA30BHzv8c8vWf2vqPbTun7Rq2GtLkPWAVQWl/0YCHQw70p93PQBtprNUmzacDInZtK6+0m87uXOP058w2ujI94WFVGhoscc73Idgrr0RojeYlalnKdMY16GZTrML0ditkHgJPFVI5CCkpTt7McBg4fUPBCk+EfcCUAsCENgIq3oOVsWdv2ujhniqsMouwxr6IVL5kFGcXLA1On897IV7oZl5pmnFlbkTQzNfg2+skTPwrQQEwP4tp4inmkTjj2M3TDU2dnZj23vpzyHzIbkB3hhlS8nURd7Crqo8e9sc9l/2aHYo2Y8TNz6bL952ofINIDweD9NmnUn1qCpCxUHCJSECIT9HXvUPll/LpPTUjqJ1hzg9GM3OzDbr/KXA/hmtO9IIguUcvBsmJdZQch2e2qDp/8sN8GBWDukPkKJjsIa8jlV5/4AEeACyP9BpKlRjkPka7cCzV7vWGNJHpxtqZuoNtO5Y7OitAzPOXmAwXfMHgyZeQJummZtSyo01WfYHkCCEdkeKj+syZ103v4GbTprO64+/hVjCJntuwKHT9unaLi7zg9Ha9gw3AbgbgVLtZtNJqo20BVwBqYbSf0PD8fk0NAkjJWejydecJp+WH34I/OMMO0Lr0IW7tpM2CIH4kcr7wTMSnb+BS6rGA2W3YgXd2S8Fr0GVF+9/jceveYZoQ4z1/74Ou520I8XlXRdYW3DfhY9yz/mPkoy16bwEwgGufHUqy45dtAqItm3z+VtfE2uMs8qE5QkVtdEZNf4k2nhWF9RAC4KTsMqmtR0z9jA0TXVNJyZiFp+/X0p42JGsMGF/94aj5pscamRHarBlZu9FR/XwKnsGc91n5teROsIagTXkxdZ/2k1XQPRW8hlvQWTI3B6rafYWg+maPxEkuCkS3DTvdbWbIfEM2nQV+FeFwGauwT6VSHHMulNY8FMt2YxZns6840U+e/NLbnj3Yiwrf3GnaqMNpxlqpHgBBWs4VEwvKPTVisRTLt2NCjrfdNJaSzjGHi0zesOVVs8YSJxN7uwqDql3zGwp8RRkf6UtKMRB4+iCSQ6F0W2F4IHM/wE9C/I3nTydp296nkTUBOhfvn2SF+97jZs+vLRbWjCpRIp7L3wsJ8ADpOJJpp/zIOc93jebvZ7CsixWXm959ze10SVt0RG240fbBgntYjwCotc5xeksSBkzH7C4bsoQPL4Atj2bsuq3uOCZfzFqhTa6qKoNVilmlt8xyPudRrUBRnArU1jPzqNzGesOD6jkf3GlNIvXpJj8a/XjIHuHwXTNnwCa+Q6t2RRtPB9i/0EbpqALdkBdnOVfffRNGhc2tQZ4gEwqwy/f/Mb7cz4yx9M4dvP12DVbY9dsh9YfC4nngKSZ4WkMst+jDSd2Y2w/4GqkAca+z7cqhP7usCUCENgKqXwYSb9PflcoQAxNzYXELNxlAlKQeqXAjCwF2fm541Pl/Rc+4sp/3sQ1x9zCZ29+lfP+wl/qePL6ma0BHiCdzFA3v56Zt79Q+MJxePrNtyC1G/LIJ29x9dNfsvLa0Xbvw1fvfdfpMRY5/ONx7/xtDx/4coOXiGAVHYIMeROpfhYZ+g7f/DSd6/41imRciDWmSDQn+O37Gk7dYiq2bWPHn8eu2RT9bUW06Qpcg6sIhLbrr6srCBG/SSuG9wNrGO5pJStforoQNVkzpn6xGGAwyP8JoA2nOqkJJ5hqDLLznB9OLr758Hvizfm5x3QqzXcf/WAaWBbu63imfmta0JOzyM9XZiH1Jnbz7YZ1UgDiX60TulsGEs8gxadhDf0Aa9hHWOVXgl2Lpr/G/fYMOPzlzlgOBVQIJZyT01VVLj/kBs6aNI2nb57NkzfM4uTNzmH6uQ+2bvPFW1/j9ecveJOxFG8/1zmbRJsuheZr8Fh1eH3KCmvEufD+b1h6lbaH3ohl3Zw1fz+Id2kI7457RyoY79rCio0ifiMtIUGevHEW6URu4FZVmhui/O/9e41iZnae80adOTYBpxktYtg4Zde38dUHGGKVYJWchjXkFSi73mULNSvJ9vtEDiL/s/KCb6UeGZYPJAaD/B8cakfbPClzkDbplQ4YtcIIgpH81nNfwMeI5YYb+7TsN+S6HBWa2Sk0X47WHli4AzGwmUntFIJ4W5teVFPYdYejC3d39OJdnJbEguD2ENoJoyrYGdrf3kHwrtimSQ98/sb7zPtsFqGwkftVW0nGUjww7XF++c50CVcML8O286/f8lgMXbJwqkrtKMTuouMqxh9Q9j3+VwACYT/7nbVbF9ew6CHFZyDlVxmeep6pi0Dppd0Sv6v7rd71sxMRKkunkz9xSBl9+rIbkfKbTE57oAqtXSH5AvnhUSHzqSEFOBD/OCg5o+3BRMDUq8pvWJSj7RSDQf4Pj06Kny4NSxP3mEAwEsSy2vbzeD2UVpUwbuuV0cSsrotPOXA8WZPuqQsRH1L5IFiFZjV2Ky1Ro7cZ0TMS5BlaEwIpRcquN0qb4b3BtyKF2UYW+NYzKpDe1aD4ZKRiOiIeVBW76XKWGbUv597xOdPf/Iwz//Md/mDbCuCtZ4zT0ArjlmXIqEosT+5n6fN7mXT0NoU/luzPTv2iw6g8sPQqCapHVXLq9MmsvnH3ZHQHCqoZ7KarsH9bG/vXFbAX7Gw6fv0bmGvIe8DbEL/P6BOlP0Y7pL/aY8KO41wnFOlkhkhRoW7WlDHK9q/T5ivweyDzLe6iZF7I5tpVW+HdkCFvIBV3I9UzsSrvXWSrj+5gMMj/wSFWGPxrk5++8ENwUs4rqnGCoQxXzz2fsZv+Dctj4fF6WGfbNbji+TWxatd3ZtCuZ3I5R8uBY2jy5U7GWIyUX+ciABaCyGFtjUWxB3CnsSmQAd8aaOYndP4EWLAtpD8zwUiqyb+V/UjpGViV92JVPYwV2a+1EK3xhyF6J15vhqISG39QGbdpE0dfYFIHlsciEDbBSUSYNutMlltrafxBH6GiICWVRfzr3uMYs4q7QBtgGEguBuOKMGTp8dzz/Q1suPO6hfdfRNDGsww7pIX9lPkYrT0ATb1DvuMTZpvkG+j89dDa/dGazbDrjnDVA9psnw0ZsdxwAuE2AkAwEmDv03curIwqQdOA1PGsqQ+wG6Zg1x2Nxp/K08Ppd/jH4crn1xR4V8h7WSSA+FZBFsM+ikEK5Z8Amv0FXbgHaJP5YYofPEubmYUVRrO/oQ1THFcbwLsSUjaNrI4xBbPs60Ybu1CBFDBF0c2MfkdeQdQLkUOxio/vfJzpTw39M/0RWJUQORwJtenI2/MnFNasAUwxLEtHgwqCW0L2F0fz3dHWKb0QCUzMPb9dB6l30cbzHP3zXKQSwi4rr4pYIe778cY8iuT8H2qINsYZveIIPN6uOx/txqkQe4iOHZtSeTfiW63L/QcaxmhkI/LZIRYEt3F4626BXsid4fshsBlWeb46ZDKe5LnbXuDlh+ZSXF7EpKO2Zux6c6H5Gpfz+qH4ZKzIATmv2tFboekqZywKhIzJecXtiMtqqT+g2YXogm2ch1/L/RaC0HZYpRcMyDn7gs4olINB/k8C1bTx1cz+CN6Vjc2diCmkLtjSWWK23KymXVuqX0CsEuza/dseAAXhM5x8OwF0lN4NIlVPOSbbvYfdcA7EH6RLc408BJAhc0ETJpBnf0ZEwb9uq3mHHb0Nmq4wOV9tdj1KKikcvOFYJt9wCutu18YeUTtmfF+TzxtHrPB+3coVq2bR5uuMho42gWdZpORMJDC+h9c3MNDUh2jdP9w/D+/yhkETf4z81JlbzPA7vPD8WXjOOTWLzh/nfk4Zhgx5OYdHX/BBJGHzIA92kjLrIzT7E9p0uaFJWkUQPsB8953oNv1eGOTJ/wUg4jMz2g7QxLMdAjwYnnoSjT+BRPaDbkkRp8GuNxSy1FynWcb5wZdcjHhHG5ZN5msTCHuzbA1ubDxVewzLNEVl5zl6Ny1hyEZLLkE85W0zQdcUBM4+5fzn49sJl7SxgVTjpukqO4+WYKfJuWjRkVhFh3c6KhEPUjwZiifnKmwuAqgmzfdlVRae7XpHkW8GA8YrYCWk5AzzOcYfxUwM/Jh7x01f3wK7AaxiNPkq2nyjue/8ayFFRyPeMc7Amjr5DprzG6VSb+Lq2KUxNDFzQIK8HXsCmq8wqz3PElByDlZo2653XEwxGOT/7Gi+CndKYdIpLmGah+I/0vUMOg3pj5DqV0zKhZTR4xY/duwRaPq32UwzjoLftYin0vVIGn/azHLt34xtX2gPaDidzhtRCkA8qISg/oj8jsuGE9HABnSu3ilAgODQC5BgB12S2KM5Ad4gDs3XouE9EKt7Gjj18xuZffcrLPipltUnrsK626+Jx9P/YleqGZMSi92PedTZqHd5pOhwCGyZI7AlVgUamgTxJ8m9Pp9hIqU/RUrOgZLTnAdGtdMU9xR595SEwDMcO/YoNJ7TdrzEL2hyDlQ+YuiZUmIotW7CcW5qqRLBfeVgDUiTVN74s/Og4TRsBCs0cKuGgcRgkP8TQu1myHyOqjgMCTdYrZK0UnQ4mnjaWUJ3EeitCkeOd/W286Xeg8ZzyQkU6Q/R+n+aBpMOsKPToekyWmsAqTcd/nEBfrsz3rYGlfYdtCEoOhZJvlSA6GlD+hvcA4UXrCHgWwWJHNb6eeQgOQfXB4T4IPU+dGI20YKPX/2MKdtcgJ3NkkqkefaWOSy58kguffGcgibmvTVq16ZLnAJ2u9ly5lO0/mTwPwzlN+cG+pJzUWuooXtqkzFL0ThEr0WjamzxKu5olVDWyGSnMa79zFqgaDKg0HRhh8/LNp3ITVch5VchYqFFx0LTJeTWgIJI0Qn5F+RfD/fGJD8S3r3Lz6PHaL6C/O87Ac2XGbP4PyAWv+TSIPoEO3obOn8CWncY1B2Ae9cogJjiGo62d9UMM5v2LGV+WK5uUO5NMBq9jfwCXQbSX6CZ3I5O1bSzuuhY5M1SMBB7loLgtkjlA+ah4Z9odHu8KyBlF2FFDkDtBbSpJbZHGrxL4s6pF0OtDG5b0MoQy425A2gz2nAqdvS+Tp2qbNvm/L2uJBFNkHIag+LNCb796Acev+Zx1M6nq2p2AVqzpSmWR29AG05H529qDKU7gWoKYvfhvmpJGeng5Es5r4p4sYonYw19G4qOc9Ii6bbO5sxXaEObNZ5kP8OVO5943KzKXNM/tjFKd2BF9nUct0YCfvCuiJTf4FqrEPEjFbeZ71uKzB8BKD4F8a3a6efRU6hmzTW4oQNt8o+EwZn8nwiafMXJPSe67kwP7pQzMxTPMKT07LZj2bVo7WGQ+dJwgzUNRYchQRct9+yvuJ5QvA5bpp0Al70Qd+XJQvAglffnaL9LRQclQFWIP+U+BgKmVT31qst7aWg4HRXH+Lz4FKzIfrmXENnH1DVczTDqoekiFBspoNn/4+c/0VyfTy9MxVPMufNudtv/XNS/PlI2rZVbrY3ngN1+BZYErUHrT0cqO1E3tNuLwLkhhiZnIcHNCrzt9oDIQPIV1I4Zplb0DvIf6DakP3ceWAUmFdaQ3H+Gd4bwzp2MtQ3iWxWGvGqcyTQG/nUQq7Rb+/YEIh7UGuL4IHRAH6wZf28MzuT/RDAz6m6airjdyO0gVgVW1cNI1WNI2Q3IkNewCikBBjbCnVOcdrom28Eqo3Ag6jhDDEJoEiRexF6wE3bNVthNl+dICgPGQaqlPb4jPCNNGqpgqqOd8XnTJWj6UzP0zLfmoWkNgZKzMK3rbo1ncWi+uuBs3uPzFn7P65icp15Da/dr2y45232o6f8aMS/Ajj+LXbOpaWCaP9Hkkq3yLhyTLJBOgmOnhjTOg9lFDwkA8SJkHG2XDp+1hJCiI1x36y5EfEhgfSS4xYAE+FYUHY+b0TduqaQ/CAaD/J8J9sLub5t6w9ANu4B4lzGemIWaV8AwdKwycnKnEoKiY/L8TkWCEN4L9x/SFPD+jdZgVHQYqI02nesE8u8gehu6cJfcNEfmB9fuXgA8YxD7lwJphI5IodH7sBfuhy74O1p/PFqzubGgq36VggtfbcCdT260aapHVtKRNBIIZdl235bvK2PSAen3nEDfWW1CTFdyw6mtNnXYv0DjuWj8USg+hcJyD37wb4DddDl2/clo/FHDwmkd1Ca4NrxZo9oCa3Bz3E0/POBdFimd6rC8/OaBIxEoOgFxYX4tjrDCu0DJOY4Uh4A1AkovGGTXDGIxQWBjyHyHq/RpR4jXGC330ALP9VBWOVTNQKO3GHccqwKJHIwUKEpK8SkoXiM1TMbIzBadihWeBEUHtm6nmR/RBduSG0CNkqTGZyCRPc1LvhVwl8cNgn+sMWIWXzfSRDakX4VsjTlPS44//rTxnPWOgcxXLvsJ2nw7RA6A7E9o7C6HPjgBCe/B2Y+czImbnE06mSabSiCWzdobN7H13rW5h8nOA9+aFOaiCyKC3XQZ+WmVODRfgTXkddQqR5suNg9FLExQtk3Npf4YTHE9jSafh+ZboPJBxCpCik8wnctal3t+XYhmfkS8o5DIQWh8hqGskmg7fsl5rTIEUnYZap/lGMiPhPQn2LWHGUlp3+pI0RGGadNHqGYgOQdNvGTGG1gfCW7ZrQJ1Z2hJJanqIjeXGQgMNkP9iaB2rdFTt+toC/QezMyww/csxUZvow/6INlMljeffo9P537B0CWr2WSvDSgq675JgmraSaWUunLINf402nAG4GJgEdgKq/ya1n/adUc4RcX2wd4H1XMQayhae4DjQdoZlTKACYAuDwxrBFJylpFddj1GwEjL2gsxn70NBMEqR6oeJ52KMPfJd6n9YRYr/+1pllutvsP+QaTyEcS3HPbC/SD9Zv4p/OOxKu7E/nUV3Kmmggz9uPU7VU0Z5pImUN84WLitS0dsmmAYAAAXUUlEQVSxDyL/xCo+BgC7/jRTRM1ZTVjgWxur8m5zXLsJjT1oJJ09w5Hw/ohvZZfxgCZe7PCZWSBBpOIBxJcvD9BdqKaMcXj6Y3K/jwCUXoS1COSJFycMdrz+haB2HRqdDslXjNZ1cAdodbxpoUeGoOQsszR1UPtrHU9c+xwfv/Y5o1ccwc7HbZdj7NAR8eY4J0w8m5+++oV4c4JAOIDX5+Gyl85lmdXHmLFk55sg7lmyVwbImnobrTuUfME0H4T3xyppM9uwow9A07nkUkD9ENwWq+xiExSit0H8YTPr94x2GB/tg2XLrM2tiFyKNfRtNDHb6Pbb3WVb+CByAFbxKebIdrNpl7cX0PYwCUJgAlb5jWabzA+mAUtjmAeGSX1I5SOIdxR2zVbOLL0DrCqsIa+7jkIzX7c7ZsdrK8Eaan53hc3PPcjQ93tkYK6qaM3GJp2Ue0Lwb4hVcUu3j5V37NjDRp6iwAPXPDALGKP8CTEY5P+CsG0zE7Msy2jbNN9kZnWe4YYTHlivddtfvvuNo8adRiKaIJ3M4PFa+AI+zn/6X6y2kfsM7Y4z7+Ohy55spQW2YPRKI7jl/05H6yebhinxmplbyQWujladQdV2JBnm0VGvRqqezNHrtmu2cSSSO8KPDH27Vd6g7diK1kx00bAplCoJQNFRSOQQiM9Am85zD5hu8CyJVf1827mzNcagPDHHCHKFdzffSbtVldoNaOwR4y7kWxkJtYl6aWKm4b3nBLgQlJxu/hu92jCevEsjxacggfXRzDx0wda4p/LECYqrYv+2jmEN5V8EMvSDHqVC1G4wYnKuZiBFWEPf6/axOsKuPcB0XrvCgtDeWKVn9fr4fzQMmKyBiFwC7IC5c74B/qFq7hARmQIcjJmuTFbVmX051yC6h4W/1HHVETfz1jPvoQrjth7LsTccRvXIcwruc+uUe4nWR1u1v7MZm2wmyeWH3sjtn1/lmpecc++reQEezAMjPX9/vPI9kDUFT42h9ZPR4pOQ4HZdWwY6ELGg4i4jnpb5EsTjaJZMyzdksGvdD4KA3QyetiA/76tf+PKtuVSXJFl5bToURdXsg0Vu2iYJzdejmW+R0PZ0KvHcER2K1uKpRkovgE5IImKVIkUHub8X3AottaH5EpP7t4ZC0bFABprOaGPJZD5D646A8puQwHjUKm3V7s9D8mXj0hXcHuIPkBuYLUNb7E4zlmYMXTU7H/WuTEFuR1+lePMUTdvDLnydf0H0tfD6PDBFVTMiMg2YApwqIisDewKrAEsAs0VkeS3oLDGI/kAmneHYCaez4KeFZDNm5vv2cx8wefy/uPPra/EH3PPv7z7/oau5w2//q6GprpmSinzRqY766i1YZuUYHvmZ/Lx2CpouRpsuRSOHYBUf161rEs9wpOoRNPuLmTl7lnLXgPGvYwTEOjJTrNJWG7ZsJsu0/a/htSfexuMV1B7J0BHVTHvoW8qr26V5PEsZymXmc3Jn9Qnjo1s02bBGNNbhfbeHQwgJ56oq9ges0DY5HZiqNjp/vAsNMoE2XYoEHjGspuarXY7mbaVeSvHxaPpts3rShCP9W4SUXtTlmDTzI1q7j6NP49SBWnnn7YvnIQgf2tNLzoGE9kCTr+PejRxCAl13Iv9V0CcKparO0jZh5zeAkc7/TwLuV9Wkqn4HfA2s05dzDaJrzJ3xDo21Ta0BHsDO2kQbYrz6qEshz0F7Qa6OCITc6HKw1T82yXtPRFhh7ZJO8u8ZIAWx29HEiwW2cYd4hhs6ZwGqpBSf4ASqlnmLAEEoPrt1JfL4Nc/w+oy3ScVTxJuSJKIW874NMO2Y9uqZQQjt5nRWuuXmA0j2e6TibvAsY7aXsJmZll4G3qUxBVznM/AMRTv2CgwENFpAOIzWNJaE98adXmm1dT9bxUjlE0jZlUjRcUjpBUj1nFZZg06HUD/ZBHSNYvo1EmZG7V0eU1dwnJNCO0No+15cZDsENjbGMS4+AnjG9P34fyL0J0/+IKDFb24E8GO79+Y5r+VBRA4TkXdE5J2amsElVl8w78tfSMbyc67x5gTzviikYQM7HbNNjrEDgC/gZYOd1y2orbLrCTuw4rrLESwK4vV5CBUHKa0uYeeTTu2ak65xNHZ31xfU2SEyP2A3nI5dsx123TGgUaRyBoR2Bc+yENgMqbgLK9TWoTvjhll5n082Y/HR3AhN9SYVhG95073qXQ5XzrimwTMS8Y7Bqn7G1AYqHkCqX8MKbQ+REzEPB2c2n/0BandrbbIaMEi4cArD6dYUqwIpu8psJxHnQRaE0ksQT5vXrIiFBCYiRYcjwW1azVY6g2Z/NQqkeRx/x+Wr+hUI7gJYRtVy/nrYjVN7bf4hIlglpyFVsyF0IHhXAd9YI1NReX+3xvxXQZfpGhGZDbi5DZ+uqk8425yOmabd09MBqOrNwM1gCq893X8QbVhylZEEwn7iTblL2FBxkCU7cTHa6dht+f7TH3nhnlfxB31kUhlWXHc5jr+psJSuP+Djkjln88lrn/PZm18zZFQl4yeNwx/wYTfuBbEH6bT7tmPXag+g6a/Q2t0dHnsWsl+jyZeR8uuwSs8ruF8i6t6wJJaXlOyClG4EgU2NNG/kQDT+WIdr8IN/7TbZXMipDagqNJ1HbnHTNjWJpmlIxZ29udxuQcSDRg6H5uvIF/5qS41JcFPwvw6p1wAF//p5DWu9gqYoWKfQpBF6iz+UO7bYQyghpOSkXp9WvCOR0n91veFfGF0GeVXdvLP3ReRAYHtgM22j6vwEtI8qI53XBjGAWHfbNakcXs6viflk0mYm6fF5KKsqYcIk18K72cbj4aRbjuTAc/fgu49/ZNiY6k7pky0QEVbdYCVW3SA3HSHF/wLfqkbnJPMp+WmPYGt6oDfQpmkd8uEKJNDGs6FqTsEGlgmT1ua5W19o/WxaUDWimqrlp+bsJ94xUHEb2nC6aeLBA8HtkZJOGBsaLSwXkf6/bl5d7yGRQ1EsiN5oqKtWFRSdnKc3JFbE1XugT/CMMikru+OKMWCKudEbyX/oJyB+N1p8fK8otoPoHvqUrhGRrYFTgB1Vc/hkM4A9RSQgIksBywFv9eVcg+gaHq+Hq14/n0333pBgJEAgHGDjPdbn6jcuwOfvuumpakQl47YaWzDAq2bQxBy0+SY08XzBpbaIIKFJWFWPQemlGAmDlh9xCLyjkfCevbtIMDIDbvny7K8Ucn0C2P+cPSgbWtrq3+r9//bOP0juurzjr+e7v+9Xcpe7xEDSaCngpAUULVpriwOoERgItkrAAcEfLUgtsVUaDC3FESzSOhFHZnAG5EcoGTqxGNE0CWAlMzUFyhhNATEoVJIQyI/L5bK3t7f7ffrH53t3e7vf3dvkbn9k93nNZLL3/ez3u5/vk8uzn+/zeZ73E4+S7Exww33XhX4xaPangfpgys0/t6N83BvcJmW5ddNRZJOoP4iObkPH9f6rRETwuj6DzH8GWbAdGdjqqojrgIggc/8l2BcJQiXSAZHFLu20XFtHHSVcPdSYLWaUJy8iO3E7OeMiHNtU9ZpgbDUuTp8DVqrqxvCrTGJ58s2L+gfQ/SvcRtp41oXXh/Q9UrYxyMS5Yy+69nn+Gy7rIXXRRFGNqrrCHs057ZMquif5b55TRpAsgSx4rmIVb/rwCJvu+zE//8nzLDplIRf+5YdYsKQ0pVMzT6CDf8PU1WcEokvx+teXn9vQrSENyVPQfSNeZ+UvNlVFh9fAkXtx3ZDGIPZ2pPfuKSqczYzm9zoNnfwuJH4WJJe5pjL7L58iNzyBtxAZ+M+WkA9oJFYMZcwYf/CLkPkRUytKo5A4D683LC1venTsJXTwc04rRsSl6s1dg8TLh5YA/CNrQ5pOJCB1ScWY/NFQvu9t5X62qmMubDTyA1cIpnno/DTS9dfTOjId+SE69OWiNMgoxM/C67vvmO9lJmh+t4u3R5bMyBFrdjt64Aomm3GD2/T9Z7zU8SFe1sxYj1dj5mQ2Udo1Kgejj1ct5KSZJ9HhO10IJHoK5F6YDK8oboPy4GdcXL3C04F0fALN74L02knhseS5SM/qY769EsopdEoEtPymsUgMmXMb2r3K9c6NnIh4leR/J9H0vSF57jnIPovm9yGR/ionP3M09wo6+HnIvYJTY+yFud9A4u+a7tRQJH4GzFuHHv6m26eJLHG9Xwsqr43aYE7eqJKZPfH56Udh6B+YCGOMPVPmY/JOYbLr6oJDr6Pph5wCZOwdSMeleD1/h3Zd6zZFvRNm3wEmzi2j6ClB3ndlxOspqXSdFj9MToDgiWAIqI+TVx1zRU3+Pib+3f096MFPQ/+WqiuWi5HYUqTv7tmbqFEVpidvVEfiPErXBBFIfGD6MIT6MHw7lRUgxxmdoiejYzuc5sqR78LokzD8bfTNZWh+F+L1ILHTa7LClc6rITLAZPGQx2RxVdzFz0cew99/mWtoMnwPWrHpRhXEzyZ83RV3gmo1RFXxjzyA/8afoHtPD9Q0i77YNR+klRrHE7aSN6pCem5C928HPejSF6UDpAfpuXn6k3Wo+rx46UDi75k89dDqIiGwUdAxdOh2pGAvwGmm/Bfk90H8HTPWKxdvDszbgKbXOV2XyEKk80okdlpwS3/v4u7j+wLDL6OZx5w2+zHKN0vXtejofwS2yjKp1X6Ly92vITp8p9vwrdhZbPS47nXarpiTN6pCIv0wsAkyW9DcTiR6EiQ/VF1loXQx0Se2dJApG3HRUyFxNoBbGedeCjnHh+zWiZ8093+BZsqwu5bm0eQyJ2RWRbZO2Wl73UjXZ6Frqs6K5l6Fke8zVY8l48I7mU3HXFIvkQHofww98qArVoosQjo/NesNq8dRzUN+NyrJKhw8wNQvYOP4wJy8UTUicUhdcDT6i8F5UTR1JaQfoDQjZjmMPQ/kIHmxkxTwD6KZH6L+IGWrKAtK+HXwukB1sKCkPrMZ4u+Bjj8/ytlWQfbZYAO2eCCNZrcGKpXHhnh9SPf1wPUzmWFFVBVNPwrDX8M9GeWo3HIQIAHRxRDWyN1oaszJG3VBulei+C4jBt/l2Xd9Aa/z8inv09GnnLQwymTIoljjPQkpl3OuudeCDJBiJzWCpv8VqYWTj8wj/MsnBl6YAkhzoJpDh7/l9jeq2h8B6IDIWyB5gXuqmEEnMR17HrJPu0rc5LklGv9GbTAnb9QFkQjS8yW0+3oXc/Z6S0rZVbPo4EqmOqA8zqHGJwuEEu9Huq4JxjNlVtWEpCPOEvE/dk8SJVLDUST1sdp85iygQ1914mBVO3gPOq90qY4zEPxSzbvCsvH2jBKDoX+EvgfKtg00Zg/LrjHqikgcifSHa5Vky6RVoq4B9JzbkP4NeL13TTqdyO8GErbFJCB5/mxNewoiMaTvQYgsAcYVHecivXci0UXTnt8I1B+GkfVUdvCx4M84PqTvRw9c7vrFVrp+frdrMRjWMmLk0cDBj+AapB8BHUIPfo5mKsZsVWwlbzQRFaL9Xg+SXFZ6hngw5w7XAYk8kA00U05EOsM7K83KTKMnQf8mp9WuoxA9teYZMJXQ3E50+C63vxE9Gem6duoq2X892PwOV+J0RChNmxxx9QkjG0L3NzS/Gz14nZMZloh7wplzO5L404JLlFEk1YNB7UP79GJtBObkjaZBY6dB2MpOOpDUn5UeHx9OvA8GNqLpfwN/NxL/I0ieX3NNcRGB6O/V9DPKoZp18r25V1ESMLwGl+3jQ/436OhPoPc7kxWl3kInsVCWlFMGHd1c+kWgI2hmY8n+hqrvpAryu9znTlQt/xUU9uAtqxkvlFZRG7ONOXmj4aj66KEvBdo4404+GryOQXKZq0CtgEROCLJSjp3H1z7FA7c8wr7XDrD47SfwF1+/gnd98IwZXbMWaP51dP/HnSKmZnB2Ktx4Hpde/goy8CPAyQtrxxXBxnfRqjpykrOd14+OhrViFtdGsZix/wl66xZveufQ9MNIzyr3Y2o5HP4VJaEiSbmUWaOmWEzeaDi6fwVkfoALt4w7DB/iH0TmrcOb8081Vyl87O7NrLnmbva8vJex0TF+vf1Vbl7+dZ574hc1/dxjQQ/dVNBmr9BmReRfniIHLd1fhO4vuL6rxCH2TqTvEbyBjS4UFjsz6BZVTBLpuCzk+mW088lNKZqSjkshdtpEH1lIuKezud80Hfk6YCt5o6H42R2Q+1nYCOS21yX7wvd9vnvTupLWgKMjWe658SHOfHr6Jtb1QnUs6Oo0XV47QS3BpBMVEaTzKui8Kvzt4kHvPejBq4PMIXHZTF2fR+J/WHpC7IwyoZiUy0CauG4c+h6E7FZ0dBt485HURdNKVBuzgzl5o7FkNpcf8+vT83fk8AjpoXTo2G9fPF7L+JOQ+sRRPwFJ7BTXjzX7rJOjiL+7rJa9RBehqeWQ2VCQrhqHyHykqFmJiAeJs5GgmtmoH+bkjcYiFWR461RYlOxKkuhIkDtU6ugXvHV+XeZQLSIxNP5eyP6Uqat5cX+kw62+Uxci3SvLXGW6z4hAojr5Aun5CsTPRI+sdbISyY8ERVNW6NQsWEzeaCiSuoCya42em+oyh0gkwopVyyfaAo6T6Ihz9Vdn0KawRsicW8GbN/kFKR0uZ79/C9K3Fpm/FW/ObTOqTq16LiJI6hK8/vV4A5vwulc6mWWjabCVvNFQJLoY7bnFVUBObCIKdK7ES55Tt3lcesNyItEID9/2PYYPpZm3sJfP3nEF77soJBbdYCRyAgw8CZlNaO4VJHYqJM6pi1M3jj+s/Z/RFGh+v5P0Fc9p1HtzGzMPVXJjuaoanxtGs2Dt/4ymRyLzoOOjjZ4GImIO3mgpLCZvGIbRwpiTNwzDaGHMyRuGYbQw5uQNwzBaGHPyhmEYLUxTpVCKyJvAq42eRxH9wL5GT6IJMbuEY3YJx+wSzmzZZYmqDoQNNJWTb0ZE5Nly+aftjNklHLNLOGaXcOphFwvXGIZhtDDm5A3DMFoYc/LT851GT6BJMbuEY3YJx+wSTs3tYjF5wzCMFsZW8oZhGC2MOXnDMIwWxpx8GUTkDhF5UUR+LiL/LiJzC8ZuFJGdIvJLEflwI+dZb0TkYyLyvyLii8i7i8ba1i4AIrIsuPedIrKq0fNpFCJyr4i8ISI7Co71icgWEflV8Hd4T8EWRkQWi8iPReT54P/Q9cHxmtrGnHx5tgB/oKqnAy8BNwKIyFJgBfD7wDLgLmmvlvM7gI8CTxUebHe7BPf6beAjwFLgssAm7ch9uN+BQlYBT6jqycATwc/tRg74W1VdCrwXuC74HampbczJl0FVN6tOtKLfBiwKXl8MrFPVUVX9DbATOKsRc2wEqvqCqv4yZKit7YK7152q+mtVzQLrcDZpO1T1KeBA0eGLgfuD1/cDy+s6qSZAVfeo6nPB68PAC8CJ1Ng25uSr41PAxuD1icBvC8ZeC461O+1ul3a//+lYoKp7gtevAwsaOZlGIyJvBd4J/Dc1tk1bd4YSkceBt4QMrVbV7wfvWY17zHqonnNrJNXYxTCOFVVVEWnb3G0R6QLWAytVdUhEJsZqYZu2dvKqel6lcRG5CrgQOFcnCwp2AYsL3rYoONYyTGeXMrS8Xaah3e9/OvaKyEJV3SMiC4E3Gj2hRiCu2/p64CFV/V5wuKa2sXBNGURkGXADcJGqpguGNgArRCQhIm8DTgaebsQcm4x2t8szwMki8jYRieM2oTc0eE7NxAbgk8HrTwJt90Qobsl+D/CCqn6jYKimtrGK1zKIyE4gAewPDm1T1WuCsdW4OH0O98i1MfwqrYeIXAJ8CxgABoGfqeqHg7G2tQuAiJwPrAEiwL2qemuDp9QQRORh4AM4Gd29wM3Ao8AjwO/g5MQ/rqrFm7MtjYi8H9gK/ALwg8NfxsXla2Ybc/KGYRgtjIVrDMMwWhhz8oZhGC2MOXnDMIwWxpy8YRhGC2NO3jAMo4UxJ28YhtHCmJM3DMNoYf4ff6LCCgBpIPEAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vd10OPt5cOqi",
        "colab_type": "text"
      },
      "source": [
        "Problem 2\n",
        "===\n",
        "使用你 test accuracy 最高的 autoencoder，從 trainX 中，取出 index 1, 2, 3, 6, 7, 9 這 6 張圖片\n",
        "畫出他們的原圖以及 reconstruct 之後的圖片。\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1OYYPEqcCpl",
        "colab_type": "code",
        "outputId": "107e2ffa-747d-4b8e-8389-2433ee921b52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "tags": []
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# 畫出原圖\n",
        "plt.figure(figsize=(10,4))\n",
        "indexes = [1,2,3,6,7,9]\n",
        "imgs = trainX[indexes,]\n",
        "for i, img in enumerate(imgs):\n",
        "    plt.subplot(2, 6, i+1, xticks=[], yticks=[])\n",
        "    plt.imshow(img)\n",
        "\n",
        "# 畫出 reconstruct 的圖\n",
        "inp = torch.Tensor(trainX_preprocessed[indexes,]).cuda()\n",
        "latents, recs = model(inp)\n",
        "recs = ((recs+1)/2).cpu().detach().numpy()\n",
        "recs = recs.transpose(0, 2, 3, 1)\n",
        "for i, img in enumerate(recs):\n",
        "    plt.subplot(2, 6, 6+i+1, xticks=[], yticks=[])\n",
        "    plt.imshow(img)\n",
        "  \n",
        "plt.tight_layout()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x288 with 12 Axes>",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hNhjaEnOcdtR",
        "colab_type": "text"
      },
      "source": [
        "Problem 3\n",
        "===\n",
        "在 autoencoder 的訓練過程中，至少挑選 10 個 checkpoints \n",
        "請用 model 的 train reconstruction error 對 val accuracy 作圖\n",
        "簡單說明你觀察到的現象\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IAc9Ha55cDOr",
        "colab_type": "code",
        "outputId": "66f252df-78c0-4e88-94d6-3de776496cbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "tags": []
      },
      "source": [
        "import glob\n",
        "\n",
        "checkpoints_list = sorted(glob.glob('checkpoints/checkpoint_*.pth'))\n",
        "\n",
        "# load data\n",
        "dataset = Image_Dataset(trainX_preprocessed)\n",
        "dataloader = DataLoader(dataset, batch_size=64, shuffle=False)\n",
        "\n",
        "points = []\n",
        "with torch.no_grad():\n",
        "    for i, checkpoint in enumerate(checkpoints_list):\n",
        "        print('[{}/{}] {}'.format(i+1, len(checkpoints_list), checkpoint))\n",
        "        model.load_state_dict(torch.load(checkpoint))\n",
        "        model.eval()\n",
        "        err = 0\n",
        "        n = 0\n",
        "        for x in dataloader:\n",
        "            x = x.cuda()\n",
        "            _, rec = model(x)\n",
        "            err += torch.nn.MSELoss(reduction='sum')(x, rec).item()\n",
        "            n += x.flatten().size(0) # 总共多少像素点\n",
        "        print('Reconstruction error (MSE):', err/n)\n",
        "        latents = inference(X=valX, model=model)\n",
        "        pred, X_embedded = predict(latents)\n",
        "        acc = cal_acc(valY, pred)\n",
        "        print('Accuracy:', acc)\n",
        "        points.append((err/n, acc))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "[1/9] checkpoints/checkpoint_10.pth\nReconstruction error (MSE): 0.0915282673181272\nLatents Shape: (500, 4096)\nFirst Reduction Shape: (500, 200)\nSecond Reduction Shape: (500, 2)\nAccuracy: 0.68\n[2/9] checkpoints/checkpoint_20.pth\nReconstruction error (MSE): 0.07738832387737199\nLatents Shape: (500, 4096)\nFirst Reduction Shape: (500, 200)\nSecond Reduction Shape: (500, 2)\nAccuracy: 0.708\n[3/9] checkpoints/checkpoint_30.pth\nReconstruction error (MSE): 0.06882103044846478\nLatents Shape: (500, 4096)\nFirst Reduction Shape: (500, 200)\nSecond Reduction Shape: (500, 2)\nAccuracy: 0.728\n[4/9] checkpoints/checkpoint_40.pth\nReconstruction error (MSE): 0.06325301720114315\nLatents Shape: (500, 4096)\nFirst Reduction Shape: (500, 200)\nSecond Reduction Shape: (500, 2)\nAccuracy: 0.748\n[5/9] checkpoints/checkpoint_50.pth\nReconstruction error (MSE): 0.05915147815031164\nLatents Shape: (500, 4096)\nFirst Reduction Shape: (500, 200)\nSecond Reduction Shape: (500, 2)\nAccuracy: 0.758\n[6/9] checkpoints/checkpoint_60.pth\nReconstruction error (MSE): 0.056090498606363934\nLatents Shape: (500, 4096)\nFirst Reduction Shape: (500, 200)\nSecond Reduction Shape: (500, 2)\nAccuracy: 0.684\n[7/9] checkpoints/checkpoint_70.pth\nReconstruction error (MSE): 0.053658866283940336\nLatents Shape: (500, 4096)\nFirst Reduction Shape: (500, 200)\nSecond Reduction Shape: (500, 2)\nAccuracy: 0.736\n[8/9] checkpoints/checkpoint_80.pth\nReconstruction error (MSE): 0.05113477336659151\nLatents Shape: (500, 4096)\nFirst Reduction Shape: (500, 200)\nSecond Reduction Shape: (500, 2)\nAccuracy: 0.778\n[9/9] checkpoints/checkpoint_90.pth\nReconstruction error (MSE): 0.0490844196992762\nLatents Shape: (500, 4096)\nFirst Reduction Shape: (500, 200)\nSecond Reduction Shape: (500, 2)\nAccuracy: 0.682\n"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_vIXEr5jsFUh",
        "colab_type": "code",
        "outputId": "0004de34-ebf8-4f39-a0f9-1e097d0a871e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "ps = list(zip(*points))\n",
        "plt.figure(figsize=(6,6))\n",
        "plt.subplot(211, title='Reconstruction error (MSE)').plot(ps[0])\n",
        "plt.subplot(212, title='Accuracy (val)').plot(ps[1])\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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Z/WsncC6AiEQBY4GNbRG4LxqRGsfj149gxc4yfj5ntU2AZozxCcdM9G7N/U7gA2AD8LqqrhORB0XkcrfZ80B3EckFfgI0DcF8GogWkXU4Hxh/V9XVbf0ifMklw3rxswszmJu9myc/2eztcIwxpnWTmqnqfGB+s233e/xejTOUsvnzKlvaHujumNifrcUHeOLjzaQnRHHFiOZ918YY03Hszth2ICL87upTOT0tnp/NWc3yHd8ZaGSMMR3GEn07CQ8J5pnpo+kVG8FtLy9na7GtO2uM8Q5L9O0oPiqM528+jQZVpvx5CXOW51kHrTGmw1mib2cDkqJ57+7xDO0dy33/zuauV1dSftBmvDTGdBxL9B0gOa4Lr942lvsuGMSCtYVc8uRivtlmdXtjTMewRN9BgoOEO88dyJwZ4wgOEqbNWsqfPsyhvqHR26EZYwKcJfoONrJPN+bPHM9VI1N46tNcrn12KTtLbKUqY0z7sUTvBdHhIfzxuuH8+YaR5BZVcslTi3lzhXXUGmPahyV6L5oyvDcLZo7nlF4x/OT1bGbOXsV+W5rQGNPGLNF7WUq3SGbfNo6fTh7Ee2sKuPiJxWRtt45aY0zbsUTvA4KDhLvOG8i/Z4wjKAiue3Ypf/pok3XUGmPahCV6HzKqTzfm3z2eK0ck89Qnm7nu2aXsKrWOWmPMybFE72NiIkL50/UjeHLaCDbvqeTiJxfz1so8b4dljPFjluh91BUjkpk/czyDe8Zw72vZzJy90jpqjTEnxBK9D0uNj2T2bWO59/xBzFtdwCVPLraZMI0xx80SvY8LCQ5i5vkDef32cYjAtc8s5XHrqDXGHAdL9H5idF+no/aKEck8+clmrp/1lXXUGmNaxRK9H4mJCOVxt6N2U2EFlzy5mHdWNV+n3RhjDmeJ3g81ddQO6hnDzNmruPe1VVRYR60x5ggs0fup1PhIXrttLPecP5B3VuVzyVOLWb5jn7fDMsb4IEv0fiwkOIh7zh/Ev2eMQ9W5o/bJjzdbR60x5jCW6APA6L7xzJ85nimZvXj8401Ms45aY4yHViV6EblIRHJEJFdEftnC/nARec3d/7WIpLnbbxKRVR4/jSIyom1fggHoGhHKE9NG8sT1I9hoHbXGGA/HTPQiEgw8DVwMDAFuEJEhzZrdAuxT1QHA48AjAKr6T1UdoaojgOnANlVd1ZYvwBzuypHJLJg5noE9opk5exU/sY5aYzq91lzRnw7kqupWVa0FZgNXNGtzBfCS+/sc4DwRkWZtbnCfa9pZanwkr98+jpnnDeTtVflc+tQSVuy0jlpjOqvWJPpkYJfH4zx3W4ttVLUeKAe6N2tzPfDqiYVpjldIcBD3Th7E67ePo6FRufaZpTz1yWYaGm0VK2M6mw7pjBWRM4AqVV17hP23iUiWiGQVFxd3REidxpi0eBbcM57LMnvxp482MW3WUvL2WUetMZ1JaxJ9PpDq8TjF3dZiGxEJAWKBEo/90zjK1byqzlLVMao6JjExsTVxm+PQNSKUJ6eN5PHrh7OhoIKLn1zM3Ozd3g7LGNNBWpPolwEDRSRdRMJwkvbcZm3mAje7v08FPlV3pWsRCQKuw+rzXnfVyBTm3z2eAUnR3P3qSn76ejblB62j1phAd8xE79bc7wQ+ADYAr6vqOhF5UEQud5s9D3QXkVzgJ4DnEMxzgF2qurVtQzcnok/3SP59+zjuPm8gb63M46yHP+X38zewZ3+1t0MzxrQTcS+8fcaYMWM0KyvL22F0Cut2l/PXRVuYv6aAkKAgrhqZzG0T+tE/MdrboRljjpOILFfVMS3us0RvdpQc4G+Lt/LvrDxqGxq5YEgPZkzoz8g+3bwdmjGmlSzRm1bZW1nDi19s5+Wl29lfXc8Z6fHMmNifiYMS+e5tEcYYX2KJ3hyXypp6Zn+zk+cWb6NwfzWDe8YwY0J/LsvsRUiwTY9kjC+yRG9OSG19I++syufZz7eSW1RJclwXbh2fzvWn9aFLWLC3wzPGeLBEb05KY6PyycYinvlsC8t37KNbZCg3n5nGzePS6BYV5u3wjDFYojdtKGt7Kc98toWPNxTRJTSY609L5Yfj00npFunt0Izp1CzRmza3aU8Fz3y2hbmrdqPA5cN7c/uEfgzu2dXboRnTKVmiN+0mv+wgzy/exuxlO6mqbWBSRiIzJvTn9PR4G6ljTAeyRG/aXVlVLa8s3cGLX26n5EAtI/vEMWNCfyaf0oOgIEv4xrQ3S/SmwxysbWDO8l3MWryVXaUH6Z8Yxe3n9OeKkb0JD7GROsa0F0v0psPVNzQyf20hzyzawvqC/fToGs4tZ6dzw+l9iIkI9XZ4xgQcS/TGa1SVxZv38sxnW/hySwkxESFMH9uX/zwrncSYcG+HZ0zAsERvfEL2rjKe/XwLC9YWEhocxNTRKdw2vh9pCVHeDs0Yv2eJ3viUbXsPMOvzrbyxIo+6hkYuObUXMyb0Z1hKrLdDM8ZvWaI3Pqmoopq/f7Gdf3y1g4rqes4a0J0ZE/pz9oAEG5ppzHGyRG98WkV1Hf/6eifPL9lGUUUNQ3t3ZfrYvlx0ak/iIm2KBWNawxK98Qs19Q28s3I3sxY7k6iFBAnjByYwZXhvJg/pYaN1jDkKS/TGr6gq63bv593s3cxbXUB+2UHCQoKYlJHIZZm9Oe+UJCLDQrwdpjE+xRK98VuqyoqdZcxbvZv3VhdQVFFDl9Bgzh/Sg8syezFhUCIRoXYjljGW6E1AaGhUvtlWyrzVu1mwtpDSA7XEhIcweWgPpgzvzdkDEgi1hVFMJ2WJ3gScuoZGvtxSwrzs3by/rpCK6nriIkO5+NReTMnsxRn9uhNsc+yYTsQSvQloNfUNLN60l3dX7+aj9Xuoqm0gMSacS4f14rLMXozq080mVjMBzxK96TQO1jawMKeId7N38+nGImrqG+kdG8Glmb2YMrw3w5JjbYy+CUgnnehF5CLgSSAYeE5VH262Pxx4GRgNlADXq+p2d18m8CzQFWgETlPV6iOdyxK9aSuVNfV8vH4P72bv5vPNxdQ1KH27R3KZm/QzesRY0jcB46QSvYgEA5uAyUAesAy4QVXXe7S5A8hU1RkiMg24SlWvF5EQYAUwXVWzRaQ7UKaqDUc6nyV60x7Kq+r4YF0h767ezZdbSmhoVAYkRTMlszeXDe9F/8Rob4dozEk52UQ/DnhAVS90H/8KQFV/79HmA7fNUje5FwKJwMXAjar6H60N1hK9aW97K2tYsLaQedm7+WZ7KaowpFdXpgzvzWWZvUiNt/Vvjf85WqJvzV0nycAuj8d5wBlHaqOq9SJSDnQHBgHqfhAkArNV9dEWArwNuA2gT58+rQjJmBOXEB3O9LF9mT62L4Xl1by3poB5q3fzyPsbeeT9jYxIjWPK8N5cOqwXPWMjvB2uMSetvW8vDAHOBk4DqoBP3E+dTzwbqeosYBY4V/TtHJMxh/SMjeCWs9O55ex0dpVW8d6aAt7N3s1D89bzP++t57S0eKYM783Fp/YkIdrmzzf+qTWJPh9I9Xic4m5rqU2eW7qJxemUzQM+V9W9ACIyHxgFfIIxPiY1PpIZE/ozY0J/thZXMm91AXOzd/Nfb6/lt++s5awBCZx/Sg/G9e/OwKRo68g1fqM1NfoQnM7Y83AS+jKcuvs6jzY/BoZ5dMZerarXiUg3nKR+NlALvA88rqrvHel8VqM3vkRVydlTwbxsp7yzvaQKgIToMMb26864/t05s38Cad0jLfEbrzqpGr1bc78T+ABneOULqrpORB4EslR1LvA88IqI5AKlwDT3uftE5E84Hw4KzD9akjfG14gIg3t2ZXDPrtx3YQa7SqtYuqWEpVtL+HLLXuatLgCgZ9cIxvV3Ev+4ft2tQ9f4FLthypgTpKps23uAL93E/9WWEkoO1AKQGt+Fcf2cq/1x/bvTo6t16pr2ZXfGGtMBVJVNeypZumUvX24p4ettpZQfrAOgX0LUoSv+sf26W8euaXOW6I3xgoZGZUPB/kOlnm+2lVJZUw9ARo+YbxN/endiI21RFXNyLNEb4wPqGxpZk1/Ol1tK+GprCcu2l1Jd14gIDO3dlXFu5+5pafG2mpY5bpbojfFBNfUNZO8qZ+kWp2N35c4yahsaCQ4ShiXHcqZ7xT+mbzxdwmxxFXN0luiN8QPVdQ0s37HvUKkne1cZ9Y1KaLAwMrUbY/t358z+3RnZJ47wEEv85nCW6I3xQwdq6lm2vfRQ4l+bX06jQnhIEKP7djt0xZ+ZEmcra5mTnuvGGOMFUeEhTMxIYmJGEgDlB+v4ZlvpoVLPYx9uAiAyLJgRqXEMS4klMzmOzJRYUrp1sRu4zCGW6I3xE7FdQpk8pAeTh/QAoPRALV9tLWHplhJW7SrjhSXbqGtwvqHHRYYyLDmWzJRYhrnJv1dshCX/TspKN8YEiJr6BnIKK1idV86avHLW5JeTs6eChkbn33hCdBjDkmMZlhJHpvshkGQ3cgUMK90Y0wmEhwSTmRJHZkrcoW3VdQ1sKNjPmvzyQx8An23ajJv76dE1/NAV/7CUWIYlx9rNXAHIEr0xASwiNJiRfboxsk+3Q9uqautZv3u/k/jzy1mdV8YnG/fQ9OU+Oa4LpyZ3JTMlzvkGkBxLt6gwL70C0xYs0RvTyUSGhTAmLZ4xafGHtlVU17Fu937W5JWzOr+cNXllfLBuz6H9qfFdyExu6vCNZWhyLLFd7KYuf2GJ3hhDTEQoY/s58/A0KT9Yx7r8psRfzur8Mt5bU3Bof3pClEeHr5P8o8Mtpfgi+79ijGlRbJdQzhyQwJkDEg5t23egljX535Z8sraXMjd7NwAi0D8xmsxkp96fmRLLkF6xdlevD7BEb4xptW5RYZwzKJFzBiUe2lZcUcPaps7e/DIW5+7lzZXOInTBQcKgHjGMSI1leEocw1PjGJgUTYjd4NWhbHilMabN7dlfzeo856p/1a4ysneVsb/ambmzS2gww5JjGZ4ay/DUOIanxNkNXm3ApkAwxniVqrK9pIrsXW7izytj3e791NY3AtA9KuxQ0s90r/7jbaTPcbFx9MYYrxIR0hOiSE+I4sqRyQDU1jeSU1hBdp5zxZ+dV8bCnKJDwzz7xEe6yT+WEalxDO1t9f4TZVf0xhifUVlTz5q88m+T/64ydpdXA069P6NHDMNT45yaf2ocA5NiCA6ykg9Y6cYY48eK9leTnVd+6Krfs94fGRbMqcnOFX9mSmynrvdb6cYY47eSukYweUjEocncGhuV7SUH3KTvXP2/+OX2Fuv9w916f2e/s9cSvTHGrwQFCf0So+mXGM1VI1OAb+v9qzxKPp71/r7dIw8N7xyRGsspvboSGdZ50p+VbowxAamiuo41+eXOVb9b9ilw6/0iTmfvwKQYMnpGM6hHDBk9Y+iXEE1YiH+O8T/p0o2IXAQ8CQQDz6nqw832hwMvA6OBEuB6Vd0uImnABiDHbfqVqs44kRdhjDHHIyYilDP7J3Bm/2/v7N2zv5rsXWVsKKhg054KcvZUsDCn6NBUziFBzuigQT1i3OTvfAj07R7l152+x0z0IhIMPA1MBvKAZSIyV1XXezS7BdinqgNEZBrwCHC9u2+Lqo5o47iNMea49egawQVDe3LB0J6HttXUN7Bt7wFyCt3kX1jJmvxy5q8tOFT6CQsJYkBiNBk9D/8ASI7zj47f1lzRnw7kqupWABGZDVwBeCb6K4AH3N/nAH8Rf3j1xphOLzwkmME9uzK4Z9fDtlfV1pNbVPntB8CeSr7aWsJb7vQOANHhIQxIiiajRwyDesa4/40mMTrcpz4AWpPok4FdHo/zgDOO1EZV60WkHGiaBi9dRFYC+4HfqOri5icQkduA2wD69OlzXC/AGGPaQ2RYyHcWcgFnVs/NbtlnU2EFm/ZU8tGGPbyW9W2ajIsMda78PT8AekQTF+md0T/t3e1cAPRR1RIRGQ28LSJDVXW/ZyNVnQXMAqcztp1jMsaYExbbJfQ78/kD7K2scRO/c/W/aU8Fb6/Mp6Km/lCbpJjwb8s/7ofAwKRootp5eufWHD0fSPV4nOJua6lNnoiEALFAiTpDemoAVHW5iGwBBgE2rMYYE1ASosNJGBB+2LTOqkpBeTWb9vJIpHAAACAASURBVHxb/9+0p4J/fr2D6rrGQ+1SunUho0cMY9Li+dHE/m0eW2sS/TJgoIik4yT0acCNzdrMBW4GlgJTgU9VVUUkEShV1QYR6QcMBLa2WfTGGOPDRITecV3oHdeFiRlJh7Y3NCp5+6oOq/9vKqygUUu8k+jdmvudwAc4wytfUNV1IvIgkKWqc4HngVdEJBcoxfkwADgHeFBE6oBGYIaqlrb5qzDGGD8SHCT07R5F3+5Rh40Aaq/7muyGKWOMCQBHu2HKP28BM8YY02qW6I0xJsBZojfGmABnid4YYwKcz3XGikgxsOMkDpEA7G2jcNqSxXV8LK7jY3Edn0CMq6+qJra0w+cS/ckSkawj9Tx7k8V1fCyu42NxHZ/OFpeVbowxJsBZojfGmAAXiIl+lrcDOAKL6/hYXMfH4jo+nSqugKvRG2OMOVwgXtEbY4zxEDCJXkQuEpEcEckVkV96O54mIvKCiBSJyFpvx9JERFJFZKGIrBeRdSIy09sxAYhIhIh8IyLZblz/7e2YPIlIsIisFJF53o7Fk4hsF5E1IrJKRHxmoigRiROROSKyUUQ2iMg4H4gpw32fmn72i8g93o4LQETudf/u14rIqyIS0WbHDoTSjbuu7SY81rUFbmi2rq1XiMg5QCXwsqqe6u14AESkF9BLVVeISAywHLjS2++Xu/xklKpWikgosASYqapfeTOuJiLyE2AM0FVVL/N2PE1EZDswRlV9aly4iLwELFbV50QkDIhU1TJvx9XEzRv5wBmqejL37rRFLMk4f+9DVPWgiLwOzFfVF9vi+IFyRX9oXVtVrQWa1rX1OlX9HGfqZp+hqgWqusL9vQLYgLMcpFepo9J9GOr++MSViIikAJcCz3k7Fn8gIrE405Q/D6Cqtb6U5F3nAVu8neQ9hABd3MWbIoHdbXXgQEn0La1r6/XE5Q9EJA0YCXzt3UgcbnlkFVAEfKSqPhEX8ATwc5x1FXyNAh+KyHJ3/WVfkA4UA393y13PiUiUt4NqZhrwqreDAFDVfOAxYCfOEqzlqvphWx0/UBK9OQEiEg28AdzTfB1fb1HVBlUdgbNk5eki4vVyl4hcBhSp6nJvx3IEZ6vqKOBi4MduudDbQoBRwF9VdSRwAPClvrMw4HLg396OBUBEuuFUIdKB3kCUiPxHWx0/UBJ9a9a1NR7cGvgbwD9V9U1vx9Oc+zV/IXCRt2MBzgIud2vhs4FzReQf3g3pW+7VIKpaBLyFU8r0tjwgz+Mb2RycxO8rLgZWqOoebwfiOh/YpqrFqloHvAmc2VYHD5REf2hdW/eTehrOOramBW6n5/PABlX9k7fjaSIiiSIS5/7eBadzfaN3owJV/ZWqpqhqGs7f1qeq2mZXWydDRKLcDnXc0sgFgNdHeKlqIbBLRDLcTecBXh8c4eEGfKRs49oJjBWRSPff53k4fWdtIiASvarWA03r2m4AXlfVdd6NyiEir+Ismp4hInkicou3Y8K5Qp2Oc2XaNMzsEm8HBfQCForIapwP749U1aeGMrYFEQl3h7b2aoPDVQLfiEg2zqyvxar6fhscty3cBfzT/f85Avidl+MBDn0gTsa5avYJ7jefOcAKYA1Obm6zu2QDYnilCRwisggYDvRU1Rovh9MuROQuYKiqzmiDYykwUFVz3Q+Ob4D+7ugzY4AAuaI3gcEdATQeZxTJ5R187pAOPN0M4JW2PqiqFuCUujr0vTO+zxK98SXfA74CXgRu9tzh3s37pogUi0iJiPzFY9+t7p2XFW5JZJS7XUVkgEe7F0Xkf9zfJ7qltF+ISCHOMMBuIjLPPcc+9/cUj+fHi8jfRWS3u/9td/taEZni0S5URPaKyMjmL1BE+gD9cIezisgZIlLo3rzT1OYqt9yBiJwuIktFpExECkTkL24/1JEswhnvb8whluiNL/ke8E/350IR6QGH7mCch1ODTsO5R2K2u+9a4AH3uV1xrmZLWnm+nkA80Be4Deffw9/dx32Ag8BfPNq/gnMjy1AgCXjc3f4y4Nk5ewlQoKorWzjnMGCr26/UVJs9AJzr0eZG4F/u7w3AvTgrD43D6aS74yivaQNO6cuYQyzRG58gImfjJNjX3fHqW3ASHjjDBXsDP1PVA6parapL3H0/BB5V1WXunbW5x3GnYyPwW1WtUdWDqlqiqm+oapV7x/D/AhPc+HrhDMmboar7VLVOVT9zj/MP4BIR6eo+ns6RSzNxQEWzba/ijALBHUFzibsNVV2uql+par2qbgeebYrpCCrccxhziCV64ytuBj70mK/lX3xbvkkFdjRdBTeTivOhcCKKVbW66YE7tO1ZEdkhIvuBz4E49xtFKlCqqvuaH0RVdwNfANe4w0MvxvlW0pJ9QEyzbf8CrhaRcOBqnPHdO9yYBrklpEI3pt/hXN0fSQzga1MNGC/ryA4oY1rkjpm/Dgh26+UA4ThJdjjO9BZ9RCSkhWS/C+h/hENX4ZRamvTEuZGnSfMhZz8FMnAmuSoUkRHASkDc88SLSNwR5mx5CefbRQiwtOkmphasBtI9X4uqrheRHTgfEJ5lG4C/ujHcoKoV4sy0OPUIxwY4Bcg+yn7TCdkVvfEFV+LUoofgjLcegZOwFuPU3r/Bmf/jYfcGoQgROct97nPAfSIyWhwDRKSvu28VcKM48+dcxNFLHuBcDR8EykQkHvht0w53RMsC4P/cTttQOXyqgbdx7vyciVOzb5Gq5gG5fPfu1X+5zz2Hw2/LjwH2A5UiMhj40TFewwQ3TmMOsURvfMHNwN9VdaeqFjb94HSE3oRzRT0FGIBzB2EecD2Aqv4bp5b+L5z69Ns4HazgJM4pOKWMm9x9R/ME0AXYizP6p/mNR9OBOpwhjEXAoXnMVfUgzpQS6Rz7Rpxn3WN5ehUnSX/abLrh+3Cu8iuAvwGvHemgbj/CEI79Ok0nYzdMGdNGROR+YNCxpkdwa/ErgfPcbwptdf4/4ky7+39tdUwTGCzRG9MG3FLPSmC6uwaBMT7DSjfGnCQRuRWns3aBJXnji+yK3hhjApxd0RtjTICzRG+MMQGuVTdMuWOQnwSCgedU9eFm+x8HJrkPI4EkVW1aQOJRnEmWgoCPgJl6lHpRQkKCpqWlHefLMMaYzm358uV7VTWxpX3HTPTu7d9P40zUnwcsE5G5qnpotRhVvdej/V04i00jImfiLHKR6e5egjNWeNGRzpeWlkZWVtaxwjLGGOPBvbu6Ra0p3ZwO5KrqVncxg9k4i9geiecSXQpEAGE4t7SHAr6yRqMxxnQKrUn0yThDx5rkudu+w731PB34FEBVl+Is8Fzg/nygqm22DqIxxphja+vO2GnAHFVtAHAXfTgFSMH5cDhXRMY3f5KI3CYiWSKSVVxc3MYhGWNM59aaRJ+PM0VrkxR3W0umcfjK6lcBX6lqpapW4ky2NK75k1R1lqqOUdUxiYkt9iUYY0ybWLFzH2vzy70dRodqTaJfBgwUkXR3CbNpwNzmjdyZ9boBSz027wQmiEiIiITidMRa6cYY4xX5ZQf53vPf8JPXV3k7lA51zETvzpl9J/ABTpJ+XVXXiciDIuK5CPE0YHazoZNzcBaFWIMzR3a2qr7bZtEbY0wrqSr/7801VNbUs2lPJfllB70dUodp1Th6VZ0PzG+27f5mjx9o4XkNwO0nEZ8xxrSJOcvz+GxTMdPH9uWVr3awKKeIm87oe+wnBgC7M9YYE/D27K/moXnrOT0tnv++fCgp3bqwcGPnGfhhid4YE9BUlV+/tYaa+kYemZpJUJAwKSOJL3L3UlPf4O3wOoQlemNMQJubvZuPNxRx3wUZpCdEATAxI5GDdQ18s63Uy9F1DEv0xpiAVVxRw2/nrmNknzh+cHb6oe3j+ncnLCSIRTmdo3xjid4YE7B+O3ctVTUN/GFqJsFBcmh7ZFgIY/t1Z2FOkRej6ziW6I0xAWn+mgLmrylk5vkDGZAU8539kzIS2Vp8gB0lB7wQXceyRG+MCTilB2q5/521DEuO5fZz+rXYZmJGEkCnKN9YojfGBJz/fncd5Qfr+MO1mYQEt5zm0hOiSOseyaJOUL6xRG+MCSgfrd/DO6t28+NJAxjcs+tR207MSOLLLSVU1wX2MEtL9MaYgFFeVcev31rD4J4x3DFxwDHbTxqcRE19I0u3lnRAdN5jid4YEzAeem89JQdqeeza4YSFHDu9nZEeT0RoEIs2Bnb5xhK9MSYgLMopYs7yPGZM6MepybGtek5EaDBn9k9gYU4xR1nK2u9ZojfG+L2K6jp+9eYaBiRFc/d5A4/ruZMyEtlZWsW2vYE7zNISvTHG7/1u/kb27K/mD1MzCQ8JPq7nNg2zXBjAwywt0Rtj/NoXuXt59Zud/HB8P0b26Xbcz0+Nj2RAUnRAD7O0RG+M8VsHaur5xRurSU+I4ieTB53wcSYOSuTrraUcqKlvw+h8hyV6Y4zf+sMHOeSXHeTRqZlEhB5fycbTpMFJ1DY0snRLYA6ztERvjPFL32wr5cUvt3PzuDROS4s/qWONSetGVFhwwE5yZoneGON3DtY28PM52aTGd+HnF2Wc9PHCQ4I5a0ACiwJ0mGWrEr2IXCQiOSKSKyK/bGH/4yKyyv3ZJCJlHvv6iMiHIrJBRNaLSFrbhW+M6Yz+9FEO20uqeOSaTCLDWrX09TFNzEgiv+wgm4sq2+R4vuSY75CIBANPA5OBPGCZiMxV1fVNbVT1Xo/2dwEjPQ7xMvC/qvqRiEQDjW0VvDGm81mxcx/PL9nGTWf04cz+CW123IkZiQAs3FjEoB7fndbYn7Xmiv50IFdVt6pqLTAbuOIo7W8AXgUQkSFAiKp+BKCqlapadZIxG2M6qeq6Bn4+ZzU9u0bwy4sHt+mxe8d1YXDPmICctrg1iT4Z2OXxOM/d9h0i0hdIBz51Nw0CykTkTRFZKSJ/cL8hNH/ebSKSJSJZxcWB9yYbY9rGU59sJreokt9fk0lMRGibH39iRhLLtpdSUV3X5sf2prbujJ0GzFHVpjk/Q4DxwH3AaUA/4PvNn6Sqs1R1jKqOSUxMbOOQjDGBYE1eOc9+vpVrR6cwYVD75IlJGYnUNypf5O5tl+N7S2sSfT6Q6vE4xd3Wkmm4ZRtXHrDKLfvUA28Do04kUGO8Jbeogkff38ikxxYx6/Mt3g6nU6qtb+Rnc7JJiA7jN5cNabfzjOrbjZjwEBZuDKzKQmu6q5cBA0UkHSfBTwNubN5IRAYD3YClzZ4bJyKJqloMnAtknXTUxrSzoopq3s0u4K2VeazN309wkBDbJZS/Ld7GD85KP+KqRaZ9PL0wl42FFTx/8xhiu7R9yaZJaHAQ4wclsGhTEaqKiBz7SX7gmIleVetF5E7gAyAYeEFV14nIg0CWqs51m04DZqvHIFRVbRCR+4BPxHnHlgN/a/NXYUwbqKqt58N1e3hrZT5LcvfS0KgMS47l/suGMGV4b5bv2MeMfyxnce5eJrkTYZn2t6FgP08vzOXKEb0575Qe7X6+iRlJzF9TyIaCCob0PvoKVf6iVQNQVXU+ML/ZtvubPX7gCM/9CMg8wfiMaVcNjcqXW/by1op83l9XSFVtA8lxXZgxoR9XjUxmQNK3w+zOHZxEt8hQ5izPs0TfQeoanJJNXGQov50ytEPOOdGt/y/MKepcid6YQKKqbCio4K2VebyzajdFFTXERIRwxYjeXDkimdPS4gkK+u5X9rCQIK4Ykcy/vt5JeVUdsZHtV0Iwjlmfb2Vt/n7+etMoukWFdcg5k7pGMLR3VxblFPHjScdejtAfWKI3nUZB+UHeWbWbt1bkk7OngtBgYWJGElePTGbS4KRWTYo1dXQKL365nbmrdzN9bN8OiLrz2ryngic/3sylw3px8bBeHXruSRlJ/PWzLQHzgW6J3gS0iuo6Fqwt5O2V+SzdWoIqjOoTx0NXnsplw3od91Xi0N5dGdwzhjeW51mib0cNjcrP5qwmKjyY/76iY0o2niYNTuQvC3NZnFvMZZm9O/z8bc0SvQk4dQ2NLN5czJsr8vlo/R5q6htJ6x7JzPMGctXIZPp2jzrhY4sIU0en8D/vbSC3qOKwGr5pOy8s2caqXWU8OW0ECdHhHX7+EandiIsMZeFGS/TG+AxVJTuvnLdX5vNu9m5KDtTSLTKU609L5cqRyYxMjWuzoXJXjEjm9ws2Mmd5fpvfhm9ga3Elj32Yw+QhPbh8uHeSbHCQcM7ARD7bVERjo7bYZ+NPLNEbv7artIq3Vubz9sp8tu49QFhIEJNP6cGVI5OZMCiRsJC2H++eGBPOxEGJvLUyj59dmEGwnycBX9LYqPzijdWEhwTxv1ee6tVx7BMzEpmbvZu1u8vJTInzWhxtwRK98TtlVbW8t6aAt1bkk7VjHwBj+8Vz+4R+XHRqr3a9oabJ1NEpfLKxiCW5e9vtdvzO6OWl21m2fR+PXTucpK4RXo3lnEGJiMCinGJL9MZ0hJr6BhZuLOatlXks3FhMbUMjA5Ki+dmFGVw5MpnkuC4dGs+5pyQR546pt0TfNnaWVPHI+zlMzEjkmlEtzpvYoRKiw8lMiWNhThF3nzfQ2+GcFEv0xmepKlk79vHWynzeW11A+cE6EqLDmT6uL1eNTGZo765e+2ofHhLM5cN7M3vZLsoP1nXIt4hApuqUbIKDhN9dNcxnph6YlJHIk59spvRALfEdNI6/PViiNz5nR8kB5izP4+1V+ewqPUiX0GAuHNqDq0alcFb/7j4zz8zU0Sm8vHQH760u4MYz+ng7HL/2r292snRrCb+/ehi9O/jb2dFMzEjiiY838/mmYq4c6f1vGSfKEr3xGY2NygtfbOOR9zfS0KicNSCBe88fxIVDexIV7nt/qsOSYxnUI5o5y3dZoj8J+WUH+f38jZw1oDvTTks99hM6UGZyLN2jwliUU2SJ3piTVVxRw0//nc3nm4qZPKQHD11xKj1jvdsZdyxNY+p/N38jW4or6Z8Y7e2Q/I6q8qs319CoysNXZ/pMyaZJUJAwYVAiC3OKaGhUvx1h5RvfgU2ntiiniIuf/Jyvt5bw0JWnMmv6aJ9P8k2uHJFMkMCbK/K8HYpf+vfyPD7fVMwvLx5Manykt8Np0cTBSeyrqiM7r8zboZwwS/TGa2rqG3ho3nq+//dldI8KZ+6dZzN9bF+fu6o7mqSuEUwYlMibK/JpaNRjP8EcUlhezUPz1nN6ejz/cYbvTidxzsAEggQWbSzydignzBK98YotxZVc/X9f8vySbXxvXF/eufMsMnr653QCU0enUlBezZdbAmv5ufakqvz6rTXUNTTy6DWZPn3naVxkGCP7dGPRJv9ddcoSvelQqspry3Zy2VNL2F12kL99bwwPXnFqq2aO9FXnnZJE14gQ3lhu5ZvWemfVbj7ZWMR9F2SQlnDicw91lEkZiazOK6e4osbboZwQS/Smw5QfrOPOV1fyizfWMCI1jgUzz2HykPZfMai9RYQGc/mI3ry/rpD91XXeDsfnFVfU8MC76xjVJ47/PCvd2+G0ykR3oZnP/PSq3hK96RBZ20u55MnFvL+2kJ9dmME/fniG33S4tsbU0alU1zUyf3WBt0Pxefe/s5aq2gYenTrcb0axDO3dlaSYcBbm+GedvlWJXkQuEpEcEckVkV+2sP9xEVnl/mwSkbJm+7uKSJ6I/KWtAjf+oaFRefLjzVz37FKCgmDOjHH8eNIAv/kH3lrDU2LpnxjFHCvfHNV7qwtYsLaQe88fxIAk/xmOKuIMs1y8qZj6hkZvh3PcjpnoRSQYeBq4GBgC3CAiQzzbqOq9qjpCVUcAfwbebHaYh4DP2yZk4y/yyw5yw6yvePzjTVw+vDfz7x7PyD7dvB1Wu3DG1KeStWMf2/ce8HY4Pqn0QC33v7OWzJRYbh3vHyUbT5MGJ7G/up6Vu/xvmGVrruhPB3JVdauq1gKzgSuO0v4G4NWmByIyGugBfHgygRr/smBNARc/8Tnrdpfzp+uG88S0kcREBPZ8MFeNdMbUv2Fj6lv0wNx17K+u49GpmT4zjcXxOHtgAsFBwkI/HGbZmnc7Gdjl8TjP3fYdItIXSAc+dR8HAX8E7ju5MI2/qKqt51dvruZH/1xBekIU7909nqtHpXg7rA7RMzaC8QOdMfWNNqb+MB+uK2Ru9m7unDSQwT27ejucE9I1IpQxfbuxMMf/OmTb+mN1GjBHVRvcx3cA81X1qJc4InKbiGSJSFZxsf+9icaxfvd+pvx5CbOX7WLGhP78e8aZfjF0ri1dMzqF/LKDfLW1xNuh+Izyqjp+8/ZaTunVlTsm9fd2OCdl0uAkNhTsp7C82tuhHJfWJPp8wHOmoRR3W0um4VG2AcYBd4rIduAx4Hsi8nDzJ6nqLFUdo6pjEhNtbm9/o6q8sGQbVz79BRXV9fzjljP45cWD22V1J193wZAexESEWKeshwfnrafkQC1/mJpJqB+WbDxNzHDy02eb/Kt805p3fRkwUETSRSQMJ5nPbd5IRAYD3YClTdtU9SZV7aOqaTjlm5dV9Tujdoz/2ltZww9eXMaD89YzfmACC2aO56wBCd4Oy2siQoOZMrw389cWUGFj6lmYU8QbK/L40YT+nJoc6+1wTlpGjxh6xUawcKN/VR6OmehVtR64E/gA2AC8rqrrRORBEbnco+k0YLaqWnGyk1i8uZiLn1zMF1tK+O/Lh/LczWPoHh3u7bC87ppRKVTXNbJgTaG3Q/Gq/dV1/L831zAwKZq7zhvg7XDahIgwMSOJJbl7qa33n2GWrZqmWFXnA/Obbbu/2eMHjnGMF4EXjys645Nq6xv544c5PPv5VgYkRfPyD07nlF7+2cHWHkb1iaNfQhRzVuRxnY/Nr96RHl6wkT37q/nrHWcRHuK/U1w0NykjkVe/2UnWjlLO7O8f3179u2BmOty2vQeY+syXPPv5Vm48ow/v3nm2JflmRIRrRqfwzbZSdpR0zjH1m/ZUMPubndx8ZhojUv17Ye3mzhyQQGiwsMiPRt9YojetoqrMWZ7HpU8tZkdJFc/8xyh+d9UwuoQFzpVaW7p6VDIi8MaKI41bCGyPfZBDVFgId5/r34tqtyQ6PITT0+NZ5EfTIViiN8e0v7qOmbNXcd+/szk1OZYFM8dz0am9vB2WT+sV24WzByTw5oq8TjemfvmOfXy4fg+3ndOPbn68oPbRTMpIYtOeSvL2VXk7lFaxRG+OasXOfVz61GLeW1PATycP4tVbx/rU4s2+bOroFPL2HeTrbaXeDqXDqCqPvL+RhOhwfnC2/01z0FpNs1n6S/nGEr1pUUOj8vTCXK59Zimq8Prt47jrvIEBNxlZe7pgSE9iwjvXmPrPNhXzzbZS7j5vgE8u6N5W+idGkRrfxW/KN5bozXcUlldz03Nf8YcPcrhkWC/mzxzP6L6BORlZe+oSFsylmb1YsLaAAzX13g6n3TU2Ko++n0NqfBemndbH2+G0KxFh4qAkvsgtoaa+4dhP8DJL9OYwH64r5KInP2d1Xjl/mJrJU9NG0DXAJyNrT1NHp1BV28CCtYE/pn7emgLWF+znp5MzOsVd0ZMGJ3KwroFv/KA0F/j/N0yrVNc18Ju313DbK8tJ6daFeXedzbVjUv1qoW5fNLpvN9K6RzJn+a5jN/ZjdQ3OvRWDe8Zw+fDe3g6nQ4zrl0BYSJBf3CVrid6QU1jB5X9Zwj++2smt49N580dn0S/RfxaF8GUiwjWjUvhqaym7Sv1jhMaJmL1sFztKqvj5RRk+vdB3W+oSFsy4ft39ok5vib4TU1VeXrqdKX9ZQumBOl76wen8+tIhneJrd0e6enQKIvBmgI6pr6qt56lPNnN6WjyT3NEoncXEjES27j3g8zfG2b/oTqr0QC23vbKc+99Zx5n9u/P+PeOZMMhmDm0PyXFdOLN/d+as2BWQY+r//sV2iitq+PlFGZ2u1DfJT4ZZWqLvhL7cspeLn/ycz3KK+a/LhvDCzaeRYJORtatrRqWwq/Qgy7b7fsfd8SirquWZz7Zw/ilJjEmL93Y4HS4tIYr0hCifXzTcEn0nUtfQyKPvb+Sm574mKjyEN+84k1vOTu80NVVvuujUnkSFBQfcMoN//WwLlTX13HdhhrdD8ZqJGYks3VLCwVrfHWZpib6T2FFygKnPLOX/Fm3h+jGpzLvr7ICYH9xfRIaFcGlmL95bXUBVbWCMqS8oP8iLX2znqhHJfrs8YFuYlJFETX2jT68qZom+E3h7ZT6XPrWEbcWVPH3jKB6+JpPIsMC9a9FXTR2dyoHaBt4PkDH1T32ymUZV7p08yNuheNXp6fF0CQ326dE3lugDWGVNPT95bRX3vLaKU3rFsOCec7g00yYj85bT0rrRJz4yIKZE2FJcyetZedx0Rl9S4yO9HY5XRYQGc2b/7izMKcZX112yRB+gsneVcelTi3l7VT73nD+QV28dS7JNRuZVTWPql24t8ZtZD4/kTx9uIjwkiDvPDYyVo07WxMFJ7CytYute3xxmaYk+wDQ2Kn9dtIVr/vol9Q3Ka7eP457zBxHi54syB4qrRyWjCm/58Zj61XllvLemgB+O72ejtVwT3aHJCzf6ZvnG/vUHkD37q5n+wtc88v5GLhjag/l3j+e0TjjkzZelxkcytl88b6zI89mv+cfy6Ps5dIsM5dbxgTsN8fFKjY9kQFI0n23yzfH0rUr0InKRiOSISK6I/LKF/Y+LyCr3Z5OIlLnbR4jIUhFZJyKrReT6tn4BxvHJhj1c/ORiVuwo4+Grh/H0jaOIjbTJyHzR1NGpbC+pYvmOfd4O5bgt2byXJbl7+fGkAcTYZHeHmZSRyNdbS31yptJjJnoRCQaeBi4GhgA3iMgQzzaqeq+qjlDVEcCfgTfdXVXA91R1KHAR8ISIBNYCkl5WXdfAA3PXcctLWfTsGsG7d53N/2/v3qOrKs88jn+f3EgIuUAuXBIuQUIiogKJSQTBtg6VXAAAFcJJREFUoKjQKq1KO1C0KArWUasdq3ams9qOszqzpnVsreOyBcEbCGMRFR0UsYCIGgggiNwDcgkBkgC5k/szf5wDPYZbAudkn5w8n7WyVs7OPvv8AsmTfd797PednNWv092h2JFMGNqLrmHBHe6irKryu2U7SIqN4K6c/k7H8Ttj0xKpb2rm8z3+12bZmjP6LKBAVfeqaj2wEPjeefafAiwAUNVdqrrb/XkRUAzYffZesvtoJd9/4TNe+Xwf00el8PZDIxmUaJOR+bvILiFMGNqb97867Nc32bT04ddH+KqwnMfGpRIeamsFt5Q5oAeRYcF+eZdsawp9EuA5x2qhe9sZRKQ/kAKsOMvXsoAwYM9ZvjZTRNaLyPqSEv8c4/Inqsq8vP3c+vwaSirrePmea/jVbUPoEmK/fB3FpIxkquoaWba1Y/TUNzY18/uPdpKa2I07RiQ7HccvhYUEcV1qPKt2FPvd9RdvX4ydDCxS1W+dpohIb+B14F5VbW75JFWdpaqZqpqZkGAn/OdTVlPPT+Zt4F/f+ZqslB588NhoxqZ3rhkDA0F2Sg+Su0d0mCkRFm0oZG9JNT+/Jc2WkzyP3LREispr2V1c5XSUb2lNoT8E9PV4nOzedjaTcQ/bnCIi0cD/Ab9U1byLCWlc8vYeY8Jzn7JiRzG//M7lvHpvFolR4U7HMhchKMjVU7+moJSispNOxzmv2oYm/vjxbob3i+XmIT2djuPXctP8s82yNYU+H0gVkRQRCcNVzJe03ElE0oHuwBce28KAt4HXVHWRdyJ3Po3u1XumzM4jPDSYxQ+OYsaYgTYZWQd354hkV0/9l/7dU//aF/s4UlHLU+PT7SL/BfSOiSC9V5TfjdNfsNCraiPwMLAM2A68qapbReRpEZnosetkYKF+e3Dqh8AY4B6P9sthXswf8A4er+GHf/mC51cUMGlEMu8/ch1XJttkZIGgX1xXslJ68NYG/+2pLz/ZwAsr93D94ARyBsY5HadDGJueyPp9J6isbXA6ymmtmtlKVZcCS1ts+1WLx785y/PmAfMuIV+ntmRzEb9cvAWAP00Z3mnW4uxMJmUk8+Sir9h4oIyM/t2djnOG2av3Un6ygSc68TTEbZU7OIEXV+3hs4JSxg/1j7ml7M5YP1Rd18jP/7qZny74ktSe3Vj66Ggr8gHqO1f2JiLUP3vqiytrmbPmG267uo9Nad0GI/p3Jyo8xK8WDbdC72e2FJZz6/NreGtjIY/cMIg3H7i2088OGMi6dQlhwtBevL+5iNoG/+qpf/5vBTQ0NfN4J5+GuK1Cg4MYk5rAyp3+02Zphd5PNDcrs1bv4Y4XP6O2oYkFM3J4/OY0m4ysE5iUkUxlXSMfbTvqdJTT9h+rZsG6A/zDNX0ZEB/pdJwOJzctgeLKOrYdrnA6CmCF3i8UV9Yy7eV1/MfSHdyQnsgHj462C1+dSM7AOJJiI/xq+ObZ5bsICRYevTHV6Sgd0vXuNkt/WTTcCr3DVu4oZsIfPyV/33F+e/tQ/nxXBrFdw5yOZdpRUJBwx4gk1uwu4Uh5rdNx2FpUzrubipg+KoXEaLtP42IkRoUzNCnab1adskLvkLrGJp5+bxv3vpJPQlQX3nv4OqZm97c+5U7qzhHJNPtJT/0zy3YSExHKA9df5nSUDm1sWiIb9p+gvMb5Nksr9A4oKK7i9hc+Z+5n33DPyAG889AoUntGOR3LOGhAfCSZ/buzaMNBRy/grd17jJU7S3gw9zJiImwa4kuRm5ZIs8Lq3c4P31ihb0eqysJ1B7jt+TUcqahlzrRMfjPxCpsJ0ACui7J7SqrZdLDMkddXVf7rwx30jO7CtGsHOJIhkAzrG0ts11C/uEvWCn07Ka9p4KE3NvKLxVsY0T+WDx4dzY2X27wh5u++c1VvwkODHJvo7OPtxWw8UMZj4wYTEWYnH5cqOEgYk5rA6l0lNDc722Zphb4d5O87zoTnVvPR1qM8NT6d16dn09MucpkWosNDGX9FL5Zsav+e+qZm5ffLdjAwPpIfZNg0xN4yNj2B0qp6vi4qdzSHFXofW7b1CFNm5REaEsSiB0fyYO5lNhmZOac7M5KpqG3k4+3t21P/zpeH2HW0yu7d8LIxqQmI4PhdsvY/6kPLtx3lofkbuTI5hvceuY5hfW0VRXN+Iy+Lp3dMeLv21Nc1NvHs8l1cmRTDhKG92u11O4O4bl24OjnW8XF6K/Q+smLHUf5x/gauSIrh1elZRNtCyqYVgt099at3lVBc0T499fPzDnCo7CRPjk+zd5s+MDYtkc2FZRyvrncsgxV6H1i5s5ifvL6Ry3tH85oVedNGd7RjT31VXSP/s7KAUYPiGJ1qq7v5Qm5aAqqwepdzwzdW6L3sk10lPPD6Bgb36sbr07OtF9m02WUJ3RjRL5ZF7TBP/Uuf7uV4dT1P3pLu09fpzK5MiiEuMszR4Rsr9F60ZncpM19bz6CEbsy7L5uYrlbkzcWZlNGX3cVVbDnku26NY1V1zF69lwlDe3G1XT/ymaAg4fq0BD7ZVUKTQ22WVui95POCUu57NZ+U+Ejm3Z9t89WYS/Ldq3oTFhLk04uyL6zcw8mGJh6/2RYV8bWxaYmU1TQ4djOcFXov+GLPMaa/ms+AuEjm359Nj0gr8ubSxESEcssVvXh3UxF1jd7vqS88UcO8vP38IKMvgxK7ef345ttGp8YTJPCJQ8M3rSr0IjJeRHaKSIGI/OIsX/+Dx5qwu0SkzONr00Rkt/tjmjfD+4O1e48x/ZV8+nbvyvwZ2cR16+J0JBMgJmUkU36ygb9t935x+MPy3SDw6Dibhrg9xHYNY0S/7qx0aNriCxZ6EQkGXgAmAEOAKSIyxHMfVf2Zqg5T1WHA88Bi93N7AL8GsoEs4Nci4n8LY16k/H3HufeVfPrEhvPGjBzircgbL7puUDw9o7vwlpeHb3YeqWTxl4XcM3IAfWIjvHpsc25j0xPZcqic4sr2n4q6NWf0WUCBqu5V1XpgIfC98+w/BVjg/vwWYLmqHlfVE8ByYPylBPYXG/Yf55656+gVHc6CGTkkRFmRN94VHCTcPjyZVbtKvFocnvloJ93CQnjQpiFuV7nuxUg+ceCsvjWFPgk46PG40L3tDCLSH0gBVrT1uR3JxgMnmDY3n8TocBbMzLHFGYzPTMpIoqlZeffLIq8cb8P+EyzfdpQHrh9Id7uW1K6G9I4mMaoLqxzop/f2xdjJwCJVbdPVIxGZKSLrRWR9SYnzczefz6aDZUybs464bmEsmJFjk5MZnxqUGMWwvt7pqT81DXF8ty7cOyrFSwlNa4kIuWmu2Swbm5rb9bVbU+gPAX09Hie7t53NZP4+bNPq56rqLFXNVNXMhAT/vTvvq8Iy7p6zlu6RriLfK8aKvPG9OzOS2Xm0kq1Fl7bQ9KpdJaz75jg/vXEQkV1CvJTOtMXYtEQqaxvZeKB92yxbU+jzgVQRSRGRMFzFfEnLnUQkHegOfOGxeRlws4h0d1+Evdm9rcP5+lA5d720lpiIUBbMzLGLWKbdTLyqD2HBl9ZT39ys/O7DnfTr0ZXJ1/TzYjrTFqNS4wkJkna/S/aChV5VG4GHcRXo7cCbqrpVRJ4WkYkeu04GFqrH+0tVPQ78O64/FvnA0+5tHcq2ogrumrOWqPBQFszIIcmKvGlHMV1DuemKnry76RD1jRf3lv+9r4rYfriCx28eTFiI3T7jlOjwUDIHdGdVO1+QbdX/uKouVdXBqnqZqv7Wve1XqrrEY5/fqOoZPfaqOldVB7k/XvZe9Pax/XAFU1/Ko2toMAtn5tC3R1enI5lOaNKIZE7UNLBiR9vPBOsbm/nvj3aR3iuK267q44N0pi1y0xLZfriCI+Xt12Zpf9rPY+eRSqa+tJbw0GAWWJE3DhqdGk9CVJeLGr753/wDHDhew1Pj020aYj8wNi0RgFXtOHxjhf4cdh+t5Eez8wgNFt6YkUP/uEinI5lOLCQ4iDuGJ7FqZzGlVXWtfl5NfSPP/a2ArAE9TvdxG2cN7tmNPjHh7TpOb4X+LAqKq5gyey1BQa4inxJvRd44786MZBqblXc3tb6n/uXP9lFaVcdTE9IQsbN5fyAi5KYnsmZ36UVfc2krK/Qt7CmpYsrsPAAWzMjhsgSb8Mn4h8E9o7gqOabVwzcnquv586o9jLu8Jxn9e/g4nWmL3MEJVNc3sX5/+/SmWKH38E1pNVNm5aGqLJiRbbP6Gb8zKSOZ7Ycr2Fp04Xnq//zJHqrqG3niFpuG2N+MGhRPaLC0W/eNFXq3fe4i39iszL8/h9SeUU5HMuYMt7l76t/acP5lBg+Xn+SVz/dx+/Ak0nrZz7K/iewSQnZKHCsvoovqYlihBw4cq2HK7DzqGpt4Y0a2/WIYv9U9MowbL0/knQv01D/38W6aVfnZuMHtmM60RW5aAruLqyg8UePz1+r0hf7gcVeRP9nQxPz7c0jvFe10JGPOa1JGMser68/ZnldQXMWb6w8yNbu/tQT7sbHpp9osfT9806kLfeGJGibPyqOqrpF592UzpI8VeeP/xgxOIL7buXvqn12+k4jQYB6+YVA7JzNtMTA+kr49Itqln77TFvpDZSeZMjuPytoG5t2XzdCkGKcjGdMqocFBfH9YH1bsKOZYi576zQfLWLrlCPePHmgL4fg5EWFsWiKfFRyjtsH7y0V66pSF/nD5SabMyqOspoHX78vmymQr8qZjOdVTv2Tzt3vqf79sJz0iw7h/tE1D3BGMTUvkZEMT677xbZtlpyv0RytqmTIrjxPV9bw2PYur+8Y6HcmYNru8dzRDk6K/NXyzZncpawpKeWjsIKLCQx1MZ1orZ2AcXUKCfH6XbKcq9MXuIl9SWccr07MY3i9glq81ndCkEclsLapg++GK04uKJMVGMDXbpiHuKCLCgskZGOfz5QU7TaEvrqxlyuw8jlTU8ur0LDL6W5E3HdvEYUmEBgtvbSjkg6+PsOVQOY+NSyU8NNjpaKYNxqYlsLe0mn2l1T57jU5R6Eur6pg6ey1FZbW8cm8WmQPsdnDT8fWIDOOGdFdP/TPLdpKa2I07RiQ7Hcu0UW47zGYZ8IX+WFUdP5qdx8ETNcy95xqyUqzIm8AxKaMvpVX17C2t5olb0gi2aYg7nAHxkQyMj2SlD4dvArrQH6+uZ+pLa9l/rIa5067h2svinI5kjFflprl66kf0i+WmIT2djmMuUm5aInl7j3Gy3jdtlgFb6E+4i/w3pdXMmXYNIwfFOx3JGK8LDQ5i8YMjmf3jTJuGuAPLTUugrrGZvL3HfHL8gCz0ZTX13DVnLXtKqpj940yuS7UibwJXv7iuxNnNUR1aVkoPIkKDfdZm2apCLyLjRWSniBSIyBnrwrr3+aGIbBORrSLyhsf237m3bReRP4mPTzvKaxq4e846dh+tYtbdGYwZbKvqGGP8W3hoMKMGxZG/74RPjh9yoR1EJBh4AbgJKATyRWSJqm7z2CcV+GdglKqeEJFE9/aRwCjgKveua4DrgVXe/CZOKT/ZwI/nrmXHkQr+cnfG6avZxhjj7/7zjquI7eqbG91ac0afBRSo6l5VrQcWAt9rsc8M4AVVPQGgqqfefygQDoQBXYBQ4Kg3grdUWdvAtLnr2Ha4ghenZnBDul2YMsZ0HAlRXQgN9s1oemuOmgQc9Hhc6N7maTAwWEQ+E5E8ERkPoKpfACuBw+6PZaq6veULiMhMEVkvIutLSi6uxaimvoma+kZe+NEIxln3gTHGnHbBoZs2HCcVyAWSgdUiciUQD1zu3gawXERGq+qnnk9W1VnALIDMzEy9mAA9o8NZ+tPRhPjoL6IxxnRUramKh4C+Ho+T3ds8FQJLVLVBVb8BduEq/LcDeapapapVwAfAtZce++ysyBtjzJlaUxnzgVQRSRGRMGAysKTFPu/gOptHROJxDeXsBQ4A14tIiIiE4roQe8bQjTHGGN+5YKFX1UbgYWAZriL9pqpuFZGnRWSie7dlwDER2YZrTP4JVT0GLAL2AFuAzcBmVX3PB9+HMcaYcxDVixoS95nMzExdv3690zGMMaZDEZENqpp5tq/ZoLYxxgQ4vzujF5ESYP8lHCIeKPVSHG+yXG1judrGcrVNIObqr6pnnQrA7wr9pRKR9ed6++Iky9U2lqttLFfbdLZcNnRjjDEBzgq9McYEuEAs9LOcDnAOlqttLFfbWK626VS5Am6M3hhjzLcF4hm9McYYDwFT6FuzOIoTRGSuiBSLyNdOZzlFRPqKyEqPhWIedToTgIiEi8g6EdnszvVvTmfyJCLBIvKliLzvdBZPIrJPRLaIyCYR8Zu7DUUkVkQWicgO98JDPpvnqg2Z0tz/Tqc+KkTkMadzAYjIz9w/91+LyAIRCffasQNh6Ma9OMouPBZHAaZ4Lo7iFBEZA1QBr6nqUKfzAIhIb6C3qm4UkShgA/B9p/+93KuPRapqlXtupDXAo6qa52SuU0Tkn4BMIFpVb3U6zykisg/IVFW/6gsXkVeBT1X1Jfc8WV1VtczpXKe468YhIFtVL+XeHW9kScL18z5EVU+KyJvAUlV9xRvHD5Qz+tYsjuIIVV0NHHc6hydVPayqG92fV+Kaw6jlGgPtTl2q3A9D3R9+cSYiIsnAd4GXnM7SEYhIDDAGmAOgqvX+VOTdbgT2OF3kPYQAESISAnQFirx14EAp9K1ZHMWchYgMAIYDa51N4uIeHtkEFAPLVdUvcgF/BJ4Emp0OchYKfCQiG0RkptNh3FKAEuBl93DXSyIS6XSoFiYDC5wOAaCqh4BncM34exgoV9WPvHX8QCn05iKISDfgLeAxVa1wOg+Aqjap6jBc6x5kiYjjw10icitQrKobnM5yDtep6ghgAvCQe7jQaSHACOBFVR0OVAP+dO0sDJgI/NXpLAAi0h3XKEQK0AeIFJG7vHX8QCn0rVkcxXhwj4G/BcxX1cVO52nJ/TZ/JTDe6Sy4Frif6B4LXwjcICLznI30d+6zwVNrNb+NayjTaYVAocc7skW4Cr+/mABsVFWfrGF9EcYB36hqiao2AIuBkd46eKAU+tYsjmLc3Bc95wDbVfVZp/OcIiIJIhLr/jwC18X1Hc6mAlX9Z1VNVtUBuH62Vqiq1862LoWIRLovqOMeGrkZcLzDS1WPAAdFJM296UbA8eYID1Pwk2EbtwNAjoh0df9+3ogXF2ny1pqxjlLVRhE5tThKMDBXVbc6HAsAEVmAa/WteBEpBH6tqnOcTcUo4G5gi3s8HOBfVHWpg5kAegOvurshgnAtcuNXrYx+qCfwtqs2EAK8oaofOhvptEeA+e6Tr73AvQ7nAU7/QbwJeMDpLKeo6loRWQRsBBqBL/HiXbIB0V5pjDHm3AJl6MYYY8w5WKE3xpgAZ4XeGGMCnBV6Y4wJcFbojTEmwFmhN8aYAGeF3hhjApwVemOMCXD/DyboQ93w9fmYAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ]
}